R Code:
#Final Project
#Load Data
library(readr)
combinedtrain <- read_csv("~/Desktop/combinedtrain.csv",
col_types = cols(ndvi_ne = col_number(),
ndvi_nw = col_number(), ndvi_se = col_number(),
ndvi_sw = col_number(), precipitation_amt_mm = col_number(),
reanalysis_air_temp_k = col_number(),
reanalysis_avg_temp_k = col_number(),
reanalysis_dew_point_temp_k = col_number(),
reanalysis_max_air_temp_k = col_number(),
reanalysis_min_air_temp_k = col_number(),
reanalysis_precip_amt_kg_per_m2 = col_number(),
reanalysis_relative_humidity_percent = col_number(),
reanalysis_sat_precip_amt_mm = col_number(),
reanalysis_specific_humidity_g_per_kg = col_number(),
reanalysis_tdtr_k = col_number(),
station_avg_temp_c = col_number(),
station_diur_temp_rng_c = col_number(),
station_max_temp_c = col_number(),
station_min_temp_c = col_number(),
station_precip_mm = col_number(),
total_cases = col_number(), weekofyear = col_number(),
year = col_number()))
test<- read_csv("~/Desktop/test.csv",
col_types = cols(ndvi_ne = col_number(),
ndvi_nw = col_number(), ndvi_se = col_number(),
ndvi_sw = col_number(), precipitation_amt_mm = col_number(),
reanalysis_air_temp_k = col_number(),
reanalysis_avg_temp_k = col_number(),
reanalysis_dew_point_temp_k = col_number(),
reanalysis_max_air_temp_k = col_number(),
reanalysis_min_air_temp_k = col_number(),
reanalysis_precip_amt_kg_per_m2 = col_number(),
reanalysis_relative_humidity_percent = col_number(),
reanalysis_sat_precip_amt_mm = col_number(),
reanalysis_specific_humidity_g_per_kg = col_number(),
reanalysis_tdtr_k = col_number(),
station_avg_temp_c = col_number(),
station_diur_temp_rng_c = col_number(),
station_max_temp_c = col_number(),
station_min_temp_c = col_number(),
station_precip_mm = col_number(),
weekofyear = col_number(),
year = col_number()))
sj.test=test[1:260,]
summary(sj.test)
## city year weekofyear week_start_date
## Length:260 Min. :2008 Min. : 1.00 Length:260
## Class :character 1st Qu.:2009 1st Qu.:13.75 Class :character
## Mode :character Median :2010 Median :26.50 Mode :character
## Mean :2010 Mean :26.50
## 3rd Qu.:2012 3rd Qu.:39.25
## Max. :2013 Max. :53.00
##
## ndvi_ne ndvi_nw ndvi_se
## Min. :-0.46340 Min. :-6.670000 Min. :0.0062
## 1st Qu.:-0.04690 1st Qu.:-0.010500 1st Qu.:0.1319
## Median : 0.01498 Median : 0.032140 Median :0.1694
## Mean : 0.02480 Mean : 0.009943 Mean :0.1771
## 3rd Qu.: 0.07990 3rd Qu.: 0.077600 3rd Qu.:0.2181
## Max. : 0.50040 Max. : 0.649000 Max. :0.3854
## NA's :43 NA's :11 NA's :1
## ndvi_sw precipitation_amt_mm reanalysis_air_temp_k
## Min. :-0.01467 Min. : 0.000 Min. :296.7
## 1st Qu.: 0.11627 1st Qu.: 2.123 1st Qu.:298.3
## Median : 0.14802 Median : 13.975 Median :299.8
## Mean : 0.15323 Mean : 26.521 Mean :299.5
## 3rd Qu.: 0.19269 3rd Qu.: 42.370 3rd Qu.:300.6
## Max. : 0.31813 Max. :169.340 Max. :301.5
## NA's :1 NA's :2 NA's :2
## reanalysis_avg_temp_k reanalysis_dew_point_temp_k
## Min. :296.8 Min. :290.8
## 1st Qu.:298.4 1st Qu.:294.0
## Median :299.8 Median :295.7
## Mean :299.5 Mean :295.3
## 3rd Qu.:300.7 3rd Qu.:296.8
## Max. :301.5 Max. :297.8
## NA's :2 NA's :2
## reanalysis_max_air_temp_k reanalysis_min_air_temp_k
## Min. :298.2 Min. :293.8
## 1st Qu.:300.3 1st Qu.:296.6
## Median :301.9 Median :297.7
## Mean :301.6 Mean :297.6
## 3rd Qu.:302.6 3rd Qu.:298.8
## Max. :304.1 Max. :299.7
## NA's :2 NA's :2
## reanalysis_precip_amt_kg_per_m2 reanalysis_relative_humidity_percent
## Min. : 0.000 Min. :64.92
## 1st Qu.: 6.588 1st Qu.:76.05
## Median : 15.245 Median :78.38
## Mean : 23.766 Mean :78.20
## 3rd Qu.: 29.600 3rd Qu.:80.42
## Max. :301.400 Max. :86.78
## NA's :2 NA's :2
## reanalysis_sat_precip_amt_mm reanalysis_specific_humidity_g_per_kg
## Min. : 0.000 Min. :12.54
## 1st Qu.: 2.123 1st Qu.:15.37
## Median : 13.975 Median :17.07
## Mean : 26.521 Mean :16.75
## 3rd Qu.: 42.370 3rd Qu.:18.20
## Max. :169.340 Max. :19.34
## NA's :2 NA's :2
## reanalysis_tdtr_k station_avg_temp_c station_diur_temp_rng_c
## Min. :1.486 Min. :24.16 Min. :4.043
## 1st Qu.:2.229 1st Qu.:26.07 1st Qu.:5.700
## Median :2.543 Median :27.42 Median :6.179
## Mean :2.587 Mean :27.27 Mean :6.152
## 3rd Qu.:2.857 3rd Qu.:28.51 3rd Qu.:6.586
## Max. :4.429 Max. :30.27 Max. :8.400
## NA's :2 NA's :2 NA's :2
## station_max_temp_c station_min_temp_c station_precip_mm
## Min. :27.20 Min. :20.00 Min. : 0.00
## 1st Qu.:30.60 1st Qu.:21.70 1st Qu.: 7.25
## Median :31.70 Median :23.30 Median : 22.35
## Mean :31.68 Mean :23.11 Mean : 34.21
## 3rd Qu.:32.80 3rd Qu.:24.40 3rd Qu.: 48.35
## Max. :35.00 Max. :26.70 Max. :207.70
## NA's :2 NA's :2 NA's :2
iq.test=test[261:416,]
#test set up
testset ="https://s3.amazonaws.com/drivendata/data/44/public/submission_format.csv"
x = read.csv(file=testset)
x2 = read.csv(file=testset)
x3 = read.csv(file=testset)
#Load Libraries
library(forecast)
## Warning: package 'forecast' was built under R version 3.4.2
## Warning in as.POSIXlt.POSIXct(Sys.time()): unknown timezone 'zone/tz/2018c.
## 1.0/zoneinfo/America/New_York'
library(fpp)
## Loading required package: fma
## Loading required package: expsmooth
## Loading required package: lmtest
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: tseries
library(caret)
## Warning: package 'caret' was built under R version 3.4.3
## Loading required package: lattice
## Loading required package: ggplot2
library(neuralnet)
library(randomForest)
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:randomForest':
##
## outlier
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(VIM)
## Loading required package: colorspace
## Loading required package: grid
## Loading required package: data.table
## Warning: package 'data.table' was built under R version 3.4.2
## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.
## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues
##
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
##
## sleep
library(mice)
## Warning: package 'mice' was built under R version 3.4.2
library(ResourceSelection)
## ResourceSelection 0.3-2 2017-02-28
library(corrplot)
## Warning: package 'corrplot' was built under R version 3.4.2
## corrplot 0.84 loaded
aggr(combinedtrain, prop=T,numbers=T)
## Warning in plot.aggr(res, ...): not enough horizontal space to display
## frequencies
##San Juan, PR
sj=combinedtrain[1:936,]
summary(sj)
## city year weekofyear week_start_date
## Length:936 Min. :1990 Min. : 1.00 Length:936
## Class :character 1st Qu.:1994 1st Qu.:13.75 Class :character
## Mode :character Median :1999 Median :26.50 Mode :character
## Mean :1999 Mean :26.50
## 3rd Qu.:2003 3rd Qu.:39.25
## Max. :2008 Max. :53.00
##
## total_cases ndvi_ne ndvi_nw ndvi_se
## Min. : 0.00 Min. :-0.40625 Min. :-0.45610 Min. :-0.01553
## 1st Qu.: 9.00 1st Qu.: 0.00450 1st Qu.: 0.01642 1st Qu.: 0.13928
## Median : 19.00 Median : 0.05770 Median : 0.06808 Median : 0.17719
## Mean : 34.18 Mean : 0.05792 Mean : 0.06747 Mean : 0.17766
## 3rd Qu.: 37.00 3rd Qu.: 0.11110 3rd Qu.: 0.11520 3rd Qu.: 0.21256
## Max. :461.00 Max. : 0.49340 Max. : 0.43710 Max. : 0.39313
## NA's :191 NA's :49 NA's :19
## ndvi_sw precipitation_amt_mm reanalysis_air_temp_k
## Min. :-0.06346 Min. : 0.00 Min. :295.9
## 1st Qu.: 0.12916 1st Qu.: 0.00 1st Qu.:298.2
## Median : 0.16597 Median : 20.80 Median :299.3
## Mean : 0.16596 Mean : 35.47 Mean :299.2
## 3rd Qu.: 0.20277 3rd Qu.: 52.18 3rd Qu.:300.1
## Max. : 0.38142 Max. :390.60 Max. :302.2
## NA's :19 NA's :9 NA's :6
## reanalysis_avg_temp_k reanalysis_dew_point_temp_k
## Min. :296.1 Min. :289.6
## 1st Qu.:298.3 1st Qu.:293.8
## Median :299.4 Median :295.5
## Mean :299.3 Mean :295.1
## 3rd Qu.:300.2 3rd Qu.:296.4
## Max. :302.2 Max. :297.8
## NA's :6 NA's :6
## reanalysis_max_air_temp_k reanalysis_min_air_temp_k
## Min. :297.8 Min. :292.6
## 1st Qu.:300.4 1st Qu.:296.3
## Median :301.5 Median :297.5
## Mean :301.4 Mean :297.3
## 3rd Qu.:302.4 3rd Qu.:298.4
## Max. :304.3 Max. :299.9
## NA's :6 NA's :6
## reanalysis_precip_amt_kg_per_m2 reanalysis_relative_humidity_percent
## Min. : 0.00 Min. :66.74
## 1st Qu.: 10.82 1st Qu.:76.25
## Median : 21.30 Median :78.67
## Mean : 30.47 Mean :78.57
## 3rd Qu.: 37.00 3rd Qu.:80.96
## Max. :570.50 Max. :87.58
## NA's :6 NA's :6
## reanalysis_sat_precip_amt_mm reanalysis_specific_humidity_g_per_kg
## Min. : 0.00 Min. :11.72
## 1st Qu.: 0.00 1st Qu.:15.24
## Median : 20.80 Median :16.85
## Mean : 35.47 Mean :16.55
## 3rd Qu.: 52.18 3rd Qu.:17.86
## Max. :390.60 Max. :19.44
## NA's :9 NA's :6
## reanalysis_tdtr_k station_avg_temp_c station_diur_temp_rng_c
## Min. :1.357 Min. :22.84 Min. :4.529
## 1st Qu.:2.157 1st Qu.:25.84 1st Qu.:6.200
## Median :2.457 Median :27.23 Median :6.757
## Mean :2.516 Mean :27.01 Mean :6.757
## 3rd Qu.:2.800 3rd Qu.:28.19 3rd Qu.:7.286
## Max. :4.429 Max. :30.07 Max. :9.914
## NA's :6 NA's :6 NA's :6
## station_max_temp_c station_min_temp_c station_precip_mm
## Min. :26.70 Min. :17.8 Min. : 0.000
## 1st Qu.:30.60 1st Qu.:21.7 1st Qu.: 6.825
## Median :31.70 Median :22.8 Median : 17.750
## Mean :31.61 Mean :22.6 Mean : 26.785
## 3rd Qu.:32.80 3rd Qu.:23.9 3rd Qu.: 35.450
## Max. :35.60 Max. :25.6 Max. :305.900
## NA's :6 NA's :6 NA's :6
#Train: make NA's Median of variable
sj$ndvi_ne <- ifelse(is.na(sj$ndvi_ne), median(sj$ndvi_ne, na.rm=TRUE), sj$ndvi_ne)
sj$ndvi_nw <- ifelse(is.na(sj$ndvi_nw), median(sj$ndvi_nw, na.rm=TRUE), sj$ndvi_nw)
sj$ndvi_se <- ifelse(is.na(sj$ndvi_se ), median(sj$ndvi_se , na.rm=TRUE), sj$ndvi_se )
sj$ndvi_sw <- ifelse(is.na(sj$ndvi_sw ), median(sj$ndvi_sw , na.rm=TRUE), sj$ndvi_sw )
sj$precipitation_amt_mm <- ifelse(is.na(sj$precipitation_amt_mm), median(sj$precipitation_amt_mm, na.rm=TRUE), sj$precipitation_amt_mm)
sj$reanalysis_air_temp_k <- ifelse(is.na(sj$reanalysis_air_temp_k), median(sj$reanalysis_air_temp_k, na.rm=TRUE), sj$reanalysis_air_temp_k)
sj$reanalysis_avg_temp_k <- ifelse(is.na(sj$reanalysis_avg_temp_k), median(sj$reanalysis_avg_temp_k, na.rm=TRUE), sj$reanalysis_avg_temp_k)
sj$reanalysis_dew_point_temp_k <- ifelse(is.na(sj$reanalysis_dew_point_temp_k), median(sj$reanalysis_dew_point_temp_k, na.rm=TRUE), sj$reanalysis_dew_point_temp_k)
sj$reanalysis_max_air_temp_k <- ifelse(is.na(sj$reanalysis_max_air_temp_k), median(sj$reanalysis_max_air_temp_k, na.rm=TRUE), sj$reanalysis_max_air_temp_k)
sj$reanalysis_min_air_temp_k <- ifelse(is.na(sj$reanalysis_min_air_temp_k), median(sj$reanalysis_min_air_temp_k, na.rm=TRUE), sj$reanalysis_min_air_temp_k)
sj$reanalysis_precip_amt_kg_per_m2 <- ifelse(is.na(sj$reanalysis_precip_amt_kg_per_m2), median(sj$reanalysis_precip_amt_kg_per_m2, na.rm=TRUE), sj$reanalysis_precip_amt_kg_per_m2)
sj$reanalysis_relative_humidity_percent <- ifelse(is.na(sj$reanalysis_relative_humidity_percent), median(sj$reanalysis_relative_humidity_percent, na.rm=TRUE), sj$reanalysis_relative_humidity_percent)
sj$reanalysis_sat_precip_amt_mm <- ifelse(is.na(sj$reanalysis_sat_precip_amt_mm), median(sj$reanalysis_sat_precip_amt_mm, na.rm=TRUE), sj$reanalysis_sat_precip_amt_mm)
sj$reanalysis_specific_humidity_g_per_kg<- ifelse(is.na(sj$reanalysis_specific_humidity_g_per_kg), median(sj$reanalysis_specific_humidity_g_per_kg, na.rm=TRUE), sj$reanalysis_specific_humidity_g_per_kg)
sj$reanalysis_tdtr_k<- ifelse(is.na(sj$reanalysis_tdtr_k), median(sj$reanalysis_tdtr_k, na.rm=TRUE), sj$reanalysis_tdtr_k)
sj$station_avg_temp_c<- ifelse(is.na(sj$station_avg_temp_c), median(sj$station_avg_temp_c, na.rm=TRUE), sj$station_avg_temp_c)
sj$station_diur_temp_rng_c<- ifelse(is.na(sj$station_diur_temp_rng_c), median(sj$station_diur_temp_rng_c, na.rm=TRUE), sj$station_diur_temp_rng_c)
sj$station_max_temp_c<- ifelse(is.na(sj$station_max_temp_c), median(sj$station_max_temp_c, na.rm=TRUE), sj$station_max_temp_c)
sj$station_min_temp_c<- ifelse(is.na(sj$station_min_temp_c), median(sj$station_min_temp_c, na.rm=TRUE), sj$station_min_temp_c)
sj$station_precip_mm<- ifelse(is.na(sj$station_precip_mm), median(sj$station_precip_mm, na.rm=TRUE), sj$station_precip_mm)
#Test: make NA's Median of variable
sj.test$ndvi_ne <- ifelse(is.na(sj.test$ndvi_ne), median(sj.test$ndvi_ne, na.rm=TRUE), sj.test$ndvi_ne)
sj.test$ndvi_nw <- ifelse(is.na(sj.test$ndvi_nw), median(sj.test$ndvi_nw, na.rm=TRUE), sj.test$ndvi_nw)
sj.test$ndvi_se <- ifelse(is.na(sj.test$ndvi_se ), median(sj.test$ndvi_se , na.rm=TRUE), sj.test$ndvi_se )
sj.test$ndvi_sw <- ifelse(is.na(sj.test$ndvi_sw ), median(sj.test$ndvi_sw , na.rm=TRUE), sj.test$ndvi_sw )
sj.test$precipitation_amt_mm <- ifelse(is.na(sj.test$precipitation_amt_mm), median(sj.test$precipitation_amt_mm, na.rm=TRUE), sj.test$precipitation_amt_mm)
sj.test$reanalysis_air_temp_k <- ifelse(is.na(sj.test$reanalysis_air_temp_k), median(sj.test$reanalysis_air_temp_k, na.rm=TRUE), sj.test$reanalysis_air_temp_k)
sj.test$reanalysis_avg_temp_k <- ifelse(is.na(sj.test$reanalysis_avg_temp_k), median(sj.test$reanalysis_avg_temp_k, na.rm=TRUE), sj.test$reanalysis_avg_temp_k)
sj.test$reanalysis_dew_point_temp_k <- ifelse(is.na(sj.test$reanalysis_dew_point_temp_k), median(sj.test$reanalysis_dew_point_temp_k, na.rm=TRUE), sj.test$reanalysis_dew_point_temp_k)
sj.test$reanalysis_max_air_temp_k <- ifelse(is.na(sj.test$reanalysis_max_air_temp_k), median(sj.test$reanalysis_max_air_temp_k, na.rm=TRUE), sj.test$reanalysis_max_air_temp_k)
sj.test$reanalysis_min_air_temp_k <- ifelse(is.na(sj.test$reanalysis_min_air_temp_k), median(sj.test$reanalysis_min_air_temp_k, na.rm=TRUE), sj.test$reanalysis_min_air_temp_k)
sj.test$reanalysis_precip_amt_kg_per_m2 <- ifelse(is.na(sj.test$reanalysis_precip_amt_kg_per_m2), median(sj.test$reanalysis_precip_amt_kg_per_m2, na.rm=TRUE), sj.test$reanalysis_precip_amt_kg_per_m2)
sj.test$reanalysis_relative_humidity_percent <- ifelse(is.na(sj.test$reanalysis_relative_humidity_percent), median(sj.test$reanalysis_relative_humidity_percent, na.rm=TRUE), sj.test$reanalysis_relative_humidity_percent)
sj.test$reanalysis_sat_precip_amt_mm <- ifelse(is.na(sj.test$reanalysis_sat_precip_amt_mm), median(sj.test$reanalysis_sat_precip_amt_mm, na.rm=TRUE), sj.test$reanalysis_sat_precip_amt_mm)
sj.test$reanalysis_specific_humidity_g_per_kg<- ifelse(is.na(sj.test$reanalysis_specific_humidity_g_per_kg), median(sj.test$reanalysis_specific_humidity_g_per_kg, na.rm=TRUE), sj.test$reanalysis_specific_humidity_g_per_kg)
sj.test$reanalysis_tdtr_k<- ifelse(is.na(sj.test$reanalysis_tdtr_k), median(sj.test$reanalysis_tdtr_k, na.rm=TRUE), sj.test$reanalysis_tdtr_k)
sj.test$station_avg_temp_c<- ifelse(is.na(sj.test$station_avg_temp_c), median(sj.test$station_avg_temp_c, na.rm=TRUE), sj.test$station_avg_temp_c)
sj.test$station_diur_temp_rng_c<- ifelse(is.na(sj.test$station_diur_temp_rng_c), median(sj$station_diur_temp_rng_c, na.rm=TRUE), sj.test$station_diur_temp_rng_c)
sj.test$station_max_temp_c<- ifelse(is.na(sj.test$station_max_temp_c), median(sj.test$station_max_temp_c, na.rm=TRUE), sj.test$station_max_temp_c)
sj.test$station_min_temp_c<- ifelse(is.na(sj.test$station_min_temp_c), median(sj.test$station_min_temp_c, na.rm=TRUE), sj.test$station_min_temp_c)
sj.test$station_precip_mm<- ifelse(is.na(sj.test$station_precip_mm), median(sj.test$station_precip_mm, na.rm=TRUE), sj.test$station_precip_mm)
summary(sj.test)
## city year weekofyear week_start_date
## Length:260 Min. :2008 Min. : 1.00 Length:260
## Class :character 1st Qu.:2009 1st Qu.:13.75 Class :character
## Mode :character Median :2010 Median :26.50 Mode :character
## Mean :2010 Mean :26.50
## 3rd Qu.:2012 3rd Qu.:39.25
## Max. :2013 Max. :53.00
## ndvi_ne ndvi_nw ndvi_se
## Min. :-0.46340 Min. :-6.670000 Min. :0.0062
## 1st Qu.:-0.03006 1st Qu.:-0.006262 1st Qu.:0.1319
## Median : 0.01498 Median : 0.032140 Median :0.1694
## Mean : 0.02318 Mean : 0.010882 Mean :0.1770
## 3rd Qu.: 0.06828 3rd Qu.: 0.076275 3rd Qu.:0.2181
## Max. : 0.50040 Max. : 0.649000 Max. :0.3854
## ndvi_sw precipitation_amt_mm reanalysis_air_temp_k
## Min. :-0.01467 Min. : 0.000 Min. :296.7
## 1st Qu.: 0.11649 1st Qu.: 2.127 1st Qu.:298.3
## Median : 0.14802 Median : 13.975 Median :299.8
## Mean : 0.15321 Mean : 26.425 Mean :299.5
## 3rd Qu.: 0.19128 3rd Qu.: 41.810 3rd Qu.:300.6
## Max. : 0.31813 Max. :169.340 Max. :301.5
## reanalysis_avg_temp_k reanalysis_dew_point_temp_k
## Min. :296.8 Min. :290.8
## 1st Qu.:298.4 1st Qu.:294.1
## Median :299.8 Median :295.7
## Mean :299.5 Mean :295.3
## 3rd Qu.:300.7 3rd Qu.:296.8
## Max. :301.5 Max. :297.8
## reanalysis_max_air_temp_k reanalysis_min_air_temp_k
## Min. :298.2 Min. :293.8
## 1st Qu.:300.4 1st Qu.:296.6
## Median :301.9 Median :297.7
## Mean :301.6 Mean :297.6
## 3rd Qu.:302.6 3rd Qu.:298.8
## Max. :304.1 Max. :299.7
## reanalysis_precip_amt_kg_per_m2 reanalysis_relative_humidity_percent
## Min. : 0.000 Min. :64.92
## 1st Qu.: 6.763 1st Qu.:76.06
## Median : 15.245 Median :78.38
## Mean : 23.701 Mean :78.20
## 3rd Qu.: 29.000 3rd Qu.:80.42
## Max. :301.400 Max. :86.78
## reanalysis_sat_precip_amt_mm reanalysis_specific_humidity_g_per_kg
## Min. : 0.000 Min. :12.54
## 1st Qu.: 2.127 1st Qu.:15.38
## Median : 13.975 Median :17.07
## Mean : 26.425 Mean :16.76
## 3rd Qu.: 41.810 3rd Qu.:18.18
## Max. :169.340 Max. :19.34
## reanalysis_tdtr_k station_avg_temp_c station_diur_temp_rng_c
## Min. :1.486 Min. :24.16 Min. :4.043
## 1st Qu.:2.229 1st Qu.:26.07 1st Qu.:5.700
## Median :2.543 Median :27.42 Median :6.193
## Mean :2.587 Mean :27.27 Mean :6.157
## 3rd Qu.:2.857 3rd Qu.:28.51 3rd Qu.:6.586
## Max. :4.429 Max. :30.27 Max. :8.400
## station_max_temp_c station_min_temp_c station_precip_mm
## Min. :27.20 Min. :20.00 Min. : 0.00
## 1st Qu.:30.60 1st Qu.:21.70 1st Qu.: 7.35
## Median :31.70 Median :23.30 Median : 22.35
## Mean :31.68 Mean :23.11 Mean : 34.12
## 3rd Qu.:32.80 3rd Qu.:24.40 3rd Qu.: 48.25
## Max. :35.00 Max. :26.70 Max. :207.70
#plots
attach(sj)
plot(ndvi_ne, total_cases)
plot( ndvi_nw, total_cases)
plot(ndvi_se, total_cases)
plot(ndvi_sw, total_cases)
plot(precipitation_amt_mm, total_cases)
#
plot(reanalysis_air_temp_k,total_cases)
plot(reanalysis_avg_temp_k,total_cases)
plot(reanalysis_dew_point_temp_k,total_cases)
plot(reanalysis_max_air_temp_k,total_cases)
plot(reanalysis_min_air_temp_k,total_cases)
plot(reanalysis_specific_humidity_g_per_kg,total_cases)
plot(station_avg_temp_c,total_cases)
plot(station_max_temp_c,total_cases)
plot(station_min_temp_c,total_cases)
#
plot(reanalysis_dew_point_temp_k,total_cases)
plot(reanalysis_max_air_temp_k,total_cases)
plot(reanalysis_min_air_temp_k,total_cases)
plot(reanalysis_precip_amt_kg_per_m2,total_cases)
plot(reanalysis_relative_humidity_percent,total_cases)
plot(reanalysis_sat_precip_amt_mm,total_cases)
plot(reanalysis_specific_humidity_g_per_kg,total_cases)
plot(reanalysis_tdtr_k,total_cases)
plot(station_avg_temp_c,total_cases)
plot(station_diur_temp_rng_c,total_cases)
plot(station_max_temp_c,total_cases)
plot(station_min_temp_c,total_cases)
plot(station_precip_mm,total_cases)
par(mfrow=c(1,3))
plot(reanalysis_dew_point_temp_k,total_cases,ylab= "Total Cases", xlab = "Mean Dew Point Temperature", main = "San Juan, Puerto Rico")#mean dew point temperature
plot(reanalysis_specific_humidity_g_per_kg,total_cases, ylab= "Total Cases", xlab = "Mean Specific Humidity")#mean specific humidity.
plot(station_avg_temp_c,total_cases, ylab= "Total Cases", xlab = "Average temperature")#average temperature
#Correlation
mycor_sj=cor(sj[,5:25])
mycor_sj
## total_cases ndvi_ne
## total_cases 1.000000e+00 0.023307674
## ndvi_ne 2.330767e-02 1.000000000
## ndvi_nw 4.978964e-02 0.611996124
## ndvi_se 5.304514e-05 0.208352630
## ndvi_sw -2.012978e-04 0.161297801
## precipitation_amt_mm 5.950978e-02 -0.042568774
## reanalysis_air_temp_k 1.818242e-01 -0.065033980
## reanalysis_avg_temp_k 1.751916e-01 -0.063396908
## reanalysis_dew_point_temp_k 2.038024e-01 -0.036017235
## reanalysis_max_air_temp_k 1.944342e-01 -0.039637851
## reanalysis_min_air_temp_k 1.879232e-01 -0.085436552
## reanalysis_precip_amt_kg_per_m2 1.070433e-01 0.004664456
## reanalysis_relative_humidity_percent 1.439411e-01 0.034458729
## reanalysis_sat_precip_amt_mm 5.950978e-02 -0.042568774
## reanalysis_specific_humidity_g_per_kg 2.079386e-01 -0.031643712
## reanalysis_tdtr_k -6.766633e-02 -0.007765380
## station_avg_temp_c 1.965916e-01 0.056741126
## station_diur_temp_rng_c 3.459698e-02 0.124353404
## station_max_temp_c 1.897796e-01 0.080881784
## station_min_temp_c 1.769835e-01 0.016480583
## station_precip_mm 5.134769e-02 -0.071149809
## ndvi_nw ndvi_se
## total_cases 0.049789641 5.304514e-05
## ndvi_ne 0.611996124 2.083526e-01
## ndvi_nw 1.000000000 1.934753e-01
## ndvi_se 0.193475251 1.000000e+00
## ndvi_sw 0.210885032 8.213534e-01
## precipitation_amt_mm -0.031411845 -1.187984e-01
## reanalysis_air_temp_k -0.075604816 -1.455038e-02
## reanalysis_avg_temp_k -0.074531726 -1.186146e-02
## reanalysis_dew_point_temp_k -0.025713549 -6.258495e-02
## reanalysis_max_air_temp_k -0.045064454 -7.338411e-03
## reanalysis_min_air_temp_k -0.073831555 -4.577488e-02
## reanalysis_precip_amt_kg_per_m2 0.009548669 -1.301078e-01
## reanalysis_relative_humidity_percent 0.074826663 -1.132023e-01
## reanalysis_sat_precip_amt_mm -0.031411845 -1.187984e-01
## reanalysis_specific_humidity_g_per_kg -0.020331608 -5.825850e-02
## reanalysis_tdtr_k -0.049357256 2.917910e-02
## station_avg_temp_c 0.084993385 -5.631050e-02
## station_diur_temp_rng_c 0.179036829 1.790613e-02
## station_max_temp_c 0.132863413 -6.311423e-02
## station_min_temp_c 0.015813529 -6.891307e-02
## station_precip_mm -0.072698900 -1.396019e-01
## ndvi_sw precipitation_amt_mm
## total_cases -0.0002012978 0.05950978
## ndvi_ne 0.1612978012 -0.04256877
## ndvi_nw 0.2108850322 -0.03141184
## ndvi_se 0.8213534420 -0.11879840
## ndvi_sw 1.0000000000 -0.11793439
## precipitation_amt_mm -0.1179343942 1.00000000
## reanalysis_air_temp_k -0.0433893109 0.23434827
## reanalysis_avg_temp_k -0.0359388765 0.22273700
## reanalysis_dew_point_temp_k -0.0877048471 0.40229780
## reanalysis_max_air_temp_k -0.0148746139 0.25700401
## reanalysis_min_air_temp_k -0.0721956337 0.24652166
## reanalysis_precip_amt_kg_per_m2 -0.1255970838 0.50941791
## reanalysis_relative_humidity_percent -0.1175842524 0.50014823
## reanalysis_sat_precip_amt_mm -0.1179343942 1.00000000
## reanalysis_specific_humidity_g_per_kg -0.0807331199 0.40965196
## reanalysis_tdtr_k 0.0521392713 -0.09335411
## station_avg_temp_c -0.0414460872 0.19646891
## station_diur_temp_rng_c 0.0692331456 -0.15824083
## station_max_temp_c -0.0177335440 0.19298753
## station_min_temp_c -0.0737458046 0.22484629
## station_precip_mm -0.1739765680 0.56641160
## reanalysis_air_temp_k
## total_cases 0.18182416
## ndvi_ne -0.06503398
## ndvi_nw -0.07560482
## ndvi_se -0.01455038
## ndvi_sw -0.04338931
## precipitation_amt_mm 0.23434827
## reanalysis_air_temp_k 1.00000000
## reanalysis_avg_temp_k 0.99749501
## reanalysis_dew_point_temp_k 0.90322987
## reanalysis_max_air_temp_k 0.93509126
## reanalysis_min_air_temp_k 0.94223856
## reanalysis_precip_amt_kg_per_m2 0.07974350
## reanalysis_relative_humidity_percent 0.29914644
## reanalysis_sat_precip_amt_mm 0.23434827
## reanalysis_specific_humidity_g_per_kg 0.90478923
## reanalysis_tdtr_k 0.17499377
## station_avg_temp_c 0.88092000
## station_diur_temp_rng_c 0.03938994
## station_max_temp_c 0.69795534
## station_min_temp_c 0.83286434
## station_precip_mm 0.11354495
## reanalysis_avg_temp_k
## total_cases 0.17519157
## ndvi_ne -0.06339691
## ndvi_nw -0.07453173
## ndvi_se -0.01186146
## ndvi_sw -0.03593888
## precipitation_amt_mm 0.22273700
## reanalysis_air_temp_k 0.99749501
## reanalysis_avg_temp_k 1.00000000
## reanalysis_dew_point_temp_k 0.89507709
## reanalysis_max_air_temp_k 0.93895420
## reanalysis_min_air_temp_k 0.93911053
## reanalysis_precip_amt_kg_per_m2 0.06187088
## reanalysis_relative_humidity_percent 0.28518725
## reanalysis_sat_precip_amt_mm 0.22273700
## reanalysis_specific_humidity_g_per_kg 0.89615740
## reanalysis_tdtr_k 0.19810993
## station_avg_temp_c 0.87912378
## station_diur_temp_rng_c 0.05396596
## station_max_temp_c 0.70332252
## station_min_temp_c 0.82711750
## station_precip_mm 0.09765180
## reanalysis_dew_point_temp_k
## total_cases 0.20380241
## ndvi_ne -0.03601724
## ndvi_nw -0.02571355
## ndvi_se -0.06258495
## ndvi_sw -0.08770485
## precipitation_amt_mm 0.40229780
## reanalysis_air_temp_k 0.90322987
## reanalysis_avg_temp_k 0.89507709
## reanalysis_dew_point_temp_k 1.00000000
## reanalysis_max_air_temp_k 0.84719605
## reanalysis_min_air_temp_k 0.89882106
## reanalysis_precip_amt_kg_per_m2 0.32795296
## reanalysis_relative_humidity_percent 0.67929889
## reanalysis_sat_precip_amt_mm 0.40229780
## reanalysis_specific_humidity_g_per_kg 0.99852804
## reanalysis_tdtr_k -0.03612925
## station_avg_temp_c 0.86894613
## station_diur_temp_rng_c -0.05670564
## station_max_temp_c 0.68976940
## station_min_temp_c 0.85016198
## station_precip_mm 0.28469973
## reanalysis_max_air_temp_k
## total_cases 0.194434157
## ndvi_ne -0.039637851
## ndvi_nw -0.045064454
## ndvi_se -0.007338411
## ndvi_sw -0.014874614
## precipitation_amt_mm 0.257004009
## reanalysis_air_temp_k 0.935091263
## reanalysis_avg_temp_k 0.938954202
## reanalysis_dew_point_temp_k 0.847196050
## reanalysis_max_air_temp_k 1.000000000
## reanalysis_min_air_temp_k 0.828477295
## reanalysis_precip_amt_kg_per_m2 0.090883506
## reanalysis_relative_humidity_percent 0.288493242
## reanalysis_sat_precip_amt_mm 0.257004009
## reanalysis_specific_humidity_g_per_kg 0.853199059
## reanalysis_tdtr_k 0.349923492
## station_avg_temp_c 0.852525376
## station_diur_temp_rng_c 0.113530506
## station_max_temp_c 0.760863830
## station_min_temp_c 0.770750472
## station_precip_mm 0.104639281
## reanalysis_min_air_temp_k
## total_cases 0.18792322
## ndvi_ne -0.08543655
## ndvi_nw -0.07383156
## ndvi_se -0.04577488
## ndvi_sw -0.07219563
## precipitation_amt_mm 0.24652166
## reanalysis_air_temp_k 0.94223856
## reanalysis_avg_temp_k 0.93911053
## reanalysis_dew_point_temp_k 0.89882106
## reanalysis_max_air_temp_k 0.82847730
## reanalysis_min_air_temp_k 1.00000000
## reanalysis_precip_amt_kg_per_m2 0.13219184
## reanalysis_relative_humidity_percent 0.38590645
## reanalysis_sat_precip_amt_mm 0.24652166
## reanalysis_specific_humidity_g_per_kg 0.89623327
## reanalysis_tdtr_k -0.05278682
## station_avg_temp_c 0.84168028
## station_diur_temp_rng_c -0.02396665
## station_max_temp_c 0.62684383
## station_min_temp_c 0.82945825
## station_precip_mm 0.15063173
## reanalysis_precip_amt_kg_per_m2
## total_cases 0.107043301
## ndvi_ne 0.004664456
## ndvi_nw 0.009548669
## ndvi_se -0.130107815
## ndvi_sw -0.125597084
## precipitation_amt_mm 0.509417912
## reanalysis_air_temp_k 0.079743502
## reanalysis_avg_temp_k 0.061870877
## reanalysis_dew_point_temp_k 0.327952959
## reanalysis_max_air_temp_k 0.090883506
## reanalysis_min_air_temp_k 0.132191840
## reanalysis_precip_amt_kg_per_m2 1.000000000
## reanalysis_relative_humidity_percent 0.602569300
## reanalysis_sat_precip_amt_mm 0.509417912
## reanalysis_specific_humidity_g_per_kg 0.333980964
## reanalysis_tdtr_k -0.307617594
## station_avg_temp_c 0.133893672
## station_diur_temp_rng_c -0.252395838
## station_max_temp_c 0.079604906
## station_min_temp_c 0.197631829
## station_precip_mm 0.478383653
## reanalysis_relative_humidity_percent
## total_cases 0.14394107
## ndvi_ne 0.03445873
## ndvi_nw 0.07482666
## ndvi_se -0.11320234
## ndvi_sw -0.11758425
## precipitation_amt_mm 0.50014823
## reanalysis_air_temp_k 0.29914644
## reanalysis_avg_temp_k 0.28518725
## reanalysis_dew_point_temp_k 0.67929889
## reanalysis_max_air_temp_k 0.28849324
## reanalysis_min_air_temp_k 0.38590645
## reanalysis_precip_amt_kg_per_m2 0.60256930
## reanalysis_relative_humidity_percent 1.00000000
## reanalysis_sat_precip_amt_mm 0.50014823
## reanalysis_specific_humidity_g_per_kg 0.67421230
## reanalysis_tdtr_k -0.37487204
## station_avg_temp_c 0.42772357
## station_diur_temp_rng_c -0.19382168
## station_max_temp_c 0.34325265
## station_min_temp_c 0.46688729
## station_precip_mm 0.44425777
## reanalysis_sat_precip_amt_mm
## total_cases 0.05950978
## ndvi_ne -0.04256877
## ndvi_nw -0.03141184
## ndvi_se -0.11879840
## ndvi_sw -0.11793439
## precipitation_amt_mm 1.00000000
## reanalysis_air_temp_k 0.23434827
## reanalysis_avg_temp_k 0.22273700
## reanalysis_dew_point_temp_k 0.40229780
## reanalysis_max_air_temp_k 0.25700401
## reanalysis_min_air_temp_k 0.24652166
## reanalysis_precip_amt_kg_per_m2 0.50941791
## reanalysis_relative_humidity_percent 0.50014823
## reanalysis_sat_precip_amt_mm 1.00000000
## reanalysis_specific_humidity_g_per_kg 0.40965196
## reanalysis_tdtr_k -0.09335411
## station_avg_temp_c 0.19646891
## station_diur_temp_rng_c -0.15824083
## station_max_temp_c 0.19298753
## station_min_temp_c 0.22484629
## station_precip_mm 0.56641160
## reanalysis_specific_humidity_g_per_kg
## total_cases 0.20793860
## ndvi_ne -0.03164371
## ndvi_nw -0.02033161
## ndvi_se -0.05825850
## ndvi_sw -0.08073312
## precipitation_amt_mm 0.40965196
## reanalysis_air_temp_k 0.90478923
## reanalysis_avg_temp_k 0.89615740
## reanalysis_dew_point_temp_k 0.99852804
## reanalysis_max_air_temp_k 0.85319906
## reanalysis_min_air_temp_k 0.89623327
## reanalysis_precip_amt_kg_per_m2 0.33398096
## reanalysis_relative_humidity_percent 0.67421230
## reanalysis_sat_precip_amt_mm 0.40965196
## reanalysis_specific_humidity_g_per_kg 1.00000000
## reanalysis_tdtr_k -0.02893894
## station_avg_temp_c 0.87003031
## station_diur_temp_rng_c -0.05967380
## station_max_temp_c 0.69102418
## station_min_temp_c 0.84926234
## station_precip_mm 0.28803736
## reanalysis_tdtr_k station_avg_temp_c
## total_cases -0.067666331 0.19659156
## ndvi_ne -0.007765380 0.05674113
## ndvi_nw -0.049357256 0.08499338
## ndvi_se 0.029179105 -0.05631050
## ndvi_sw 0.052139271 -0.04144609
## precipitation_amt_mm -0.093354111 0.19646891
## reanalysis_air_temp_k 0.174993775 0.88092000
## reanalysis_avg_temp_k 0.198109926 0.87912378
## reanalysis_dew_point_temp_k -0.036129252 0.86894613
## reanalysis_max_air_temp_k 0.349923492 0.85252538
## reanalysis_min_air_temp_k -0.052786824 0.84168028
## reanalysis_precip_amt_kg_per_m2 -0.307617594 0.13389367
## reanalysis_relative_humidity_percent -0.374872037 0.42772357
## reanalysis_sat_precip_amt_mm -0.093354111 0.19646891
## reanalysis_specific_humidity_g_per_kg -0.028938938 0.87003031
## reanalysis_tdtr_k 1.000000000 0.13571570
## station_avg_temp_c 0.135715701 1.00000000
## station_diur_temp_rng_c 0.370993189 0.18323188
## station_max_temp_c 0.279684155 0.86449248
## station_min_temp_c 0.007474441 0.89840433
## station_precip_mm -0.206055672 0.03049977
## station_diur_temp_rng_c
## total_cases 0.03459698
## ndvi_ne 0.12435340
## ndvi_nw 0.17903683
## ndvi_se 0.01790613
## ndvi_sw 0.06923315
## precipitation_amt_mm -0.15824083
## reanalysis_air_temp_k 0.03938994
## reanalysis_avg_temp_k 0.05396596
## reanalysis_dew_point_temp_k -0.05670564
## reanalysis_max_air_temp_k 0.11353051
## reanalysis_min_air_temp_k -0.02396665
## reanalysis_precip_amt_kg_per_m2 -0.25239584
## reanalysis_relative_humidity_percent -0.19382168
## reanalysis_sat_precip_amt_mm -0.15824083
## reanalysis_specific_humidity_g_per_kg -0.05967380
## reanalysis_tdtr_k 0.37099319
## station_avg_temp_c 0.18323188
## station_diur_temp_rng_c 1.00000000
## station_max_temp_c 0.47325828
## station_min_temp_c -0.12464389
## station_precip_mm -0.26522131
## station_max_temp_c
## total_cases 0.189779591
## ndvi_ne 0.080881784
## ndvi_nw 0.132863413
## ndvi_se -0.063114231
## ndvi_sw -0.017733544
## precipitation_amt_mm 0.192987528
## reanalysis_air_temp_k 0.697955345
## reanalysis_avg_temp_k 0.703322522
## reanalysis_dew_point_temp_k 0.689769404
## reanalysis_max_air_temp_k 0.760863830
## reanalysis_min_air_temp_k 0.626843830
## reanalysis_precip_amt_kg_per_m2 0.079604906
## reanalysis_relative_humidity_percent 0.343252654
## reanalysis_sat_precip_amt_mm 0.192987528
## reanalysis_specific_humidity_g_per_kg 0.691024184
## reanalysis_tdtr_k 0.279684155
## station_avg_temp_c 0.864492476
## station_diur_temp_rng_c 0.473258276
## station_max_temp_c 1.000000000
## station_min_temp_c 0.673002123
## station_precip_mm 0.005826893
## station_min_temp_c station_precip_mm
## total_cases 0.176983464 0.051347685
## ndvi_ne 0.016480583 -0.071149809
## ndvi_nw 0.015813529 -0.072698900
## ndvi_se -0.068913066 -0.139601899
## ndvi_sw -0.073745805 -0.173976568
## precipitation_amt_mm 0.224846294 0.566411604
## reanalysis_air_temp_k 0.832864344 0.113544952
## reanalysis_avg_temp_k 0.827117500 0.097651799
## reanalysis_dew_point_temp_k 0.850161982 0.284699728
## reanalysis_max_air_temp_k 0.770750472 0.104639281
## reanalysis_min_air_temp_k 0.829458255 0.150631734
## reanalysis_precip_amt_kg_per_m2 0.197631829 0.478383653
## reanalysis_relative_humidity_percent 0.466887291 0.444257765
## reanalysis_sat_precip_amt_mm 0.224846294 0.566411604
## reanalysis_specific_humidity_g_per_kg 0.849262342 0.288037355
## reanalysis_tdtr_k 0.007474441 -0.206055672
## station_avg_temp_c 0.898404333 0.030499775
## station_diur_temp_rng_c -0.124643887 -0.265221307
## station_max_temp_c 0.673002123 0.005826893
## station_min_temp_c 1.000000000 0.084888874
## station_precip_mm 0.084888874 1.000000000
corrplot(mycor_sj, type = "lower", order = "original",
tl.col = "black", tl.cex=0.70, tl.srt = 10)#SJCOR
## Warning in corrplot(mycor_sj, type = "lower", order = "original", tl.col =
## "black", : Not been able to calculate text margin, please try again with a
## clean new empty window using {plot.new(); dev.off()} or reduce tl.cex
##Regular
sjtot=ts(sj$total_cases, frequency=52, start=c(1990,04,30))
plot(sjtot, ylab= "Total Cases", main="San Juan, PR")
acf(sjtot, main="ACF: San Juan, PR")
pacf(sjtot, main="PACF: San Juan, PR")
#create xreg
xreg1<-cbind(sj$precipitation_amt_mm, sj$station_avg_temp_c)
xreg1.test<-cbind(sj.test$precipitation_amt_mm, sj.test$station_avg_temp_c)
##Iquitos, Peru
iq=combinedtrain[937:1456,]
summary(iq)
## city year weekofyear week_start_date
## Length:520 Min. :2000 Min. : 1.00 Length:520
## Class :character 1st Qu.:2003 1st Qu.:13.75 Class :character
## Mode :character Median :2005 Median :26.50 Mode :character
## Mean :2005 Mean :26.50
## 3rd Qu.:2007 3rd Qu.:39.25
## Max. :2010 Max. :53.00
##
## total_cases ndvi_ne ndvi_nw ndvi_se
## Min. : 0.000 Min. :0.06173 Min. :0.03586 Min. :0.02988
## 1st Qu.: 1.000 1st Qu.:0.20000 1st Qu.:0.17954 1st Qu.:0.19474
## Median : 5.000 Median :0.26364 Median :0.23297 Median :0.24980
## Mean : 7.565 Mean :0.26387 Mean :0.23878 Mean :0.25013
## 3rd Qu.: 9.000 3rd Qu.:0.31997 3rd Qu.:0.29393 3rd Qu.:0.30230
## Max. :116.000 Max. :0.50836 Max. :0.45443 Max. :0.53831
## NA's :3 NA's :3 NA's :3
## ndvi_sw precipitation_amt_mm reanalysis_air_temp_k
## Min. :0.06418 Min. : 0.00 Min. :294.6
## 1st Qu.:0.20413 1st Qu.: 39.10 1st Qu.:297.1
## Median :0.26214 Median : 60.47 Median :297.8
## Mean :0.26678 Mean : 64.25 Mean :297.9
## 3rd Qu.:0.32515 3rd Qu.: 85.76 3rd Qu.:298.6
## Max. :0.54602 Max. :210.83 Max. :301.6
## NA's :3 NA's :4 NA's :4
## reanalysis_avg_temp_k reanalysis_dew_point_temp_k
## Min. :294.9 Min. :290.1
## 1st Qu.:298.2 1st Qu.:294.6
## Median :299.1 Median :295.9
## Mean :299.1 Mean :295.5
## 3rd Qu.:300.1 3rd Qu.:296.5
## Max. :302.9 Max. :298.4
## NA's :4 NA's :4
## reanalysis_max_air_temp_k reanalysis_min_air_temp_k
## Min. :300.0 Min. :286.9
## 1st Qu.:305.2 1st Qu.:292.0
## Median :307.1 Median :293.1
## Mean :307.1 Mean :292.9
## 3rd Qu.:308.7 3rd Qu.:294.2
## Max. :314.0 Max. :296.0
## NA's :4 NA's :4
## reanalysis_precip_amt_kg_per_m2 reanalysis_relative_humidity_percent
## Min. : 0.00 Min. :57.79
## 1st Qu.: 24.07 1st Qu.:84.30
## Median : 46.44 Median :90.92
## Mean : 57.61 Mean :88.64
## 3rd Qu.: 71.07 3rd Qu.:94.56
## Max. :362.03 Max. :98.61
## NA's :4 NA's :4
## reanalysis_sat_precip_amt_mm reanalysis_specific_humidity_g_per_kg
## Min. : 0.00 Min. :12.11
## 1st Qu.: 39.10 1st Qu.:16.10
## Median : 60.47 Median :17.43
## Mean : 64.25 Mean :17.10
## 3rd Qu.: 85.76 3rd Qu.:18.18
## Max. :210.83 Max. :20.46
## NA's :4 NA's :4
## reanalysis_tdtr_k station_avg_temp_c station_diur_temp_rng_c
## Min. : 3.714 Min. :21.40 Min. : 5.20
## 1st Qu.: 7.371 1st Qu.:27.00 1st Qu.: 9.50
## Median : 8.964 Median :27.60 Median :10.62
## Mean : 9.207 Mean :27.53 Mean :10.57
## 3rd Qu.:11.014 3rd Qu.:28.10 3rd Qu.:11.65
## Max. :16.029 Max. :30.80 Max. :15.80
## NA's :4 NA's :37 NA's :37
## station_max_temp_c station_min_temp_c station_precip_mm
## Min. :30.1 Min. :14.7 Min. : 0.00
## 1st Qu.:33.2 1st Qu.:20.6 1st Qu.: 17.20
## Median :34.0 Median :21.3 Median : 45.30
## Mean :34.0 Mean :21.2 Mean : 62.47
## 3rd Qu.:34.9 3rd Qu.:22.0 3rd Qu.: 85.95
## Max. :42.2 Max. :24.2 Max. :543.30
## NA's :14 NA's :8 NA's :16
#Train: make NA's Median of variable
iq$ndvi_ne <- ifelse(is.na(iq$ndvi_ne), median(iq$ndvi_ne, na.rm=TRUE), iq$ndvi_ne)
iq$ndvi_nw <- ifelse(is.na(iq$ndvi_nw), median(iq$ndvi_nw, na.rm=TRUE), iq$ndvi_nw)
iq$ndvi_se <- ifelse(is.na(iq$ndvi_se ), median(iq$ndvi_se , na.rm=TRUE), iq$ndvi_se )
iq$ndvi_sw <- ifelse(is.na(iq$ndvi_sw ), median(iq$ndvi_sw , na.rm=TRUE), iq$ndvi_sw )
iq$precipitation_amt_mm <- ifelse(is.na(iq$precipitation_amt_mm), median(iq$precipitation_amt_mm, na.rm=TRUE), iq$precipitation_amt_mm)
iq$reanalysis_air_temp_k <- ifelse(is.na(iq$reanalysis_air_temp_k), median(iq$reanalysis_air_temp_k, na.rm=TRUE), iq$reanalysis_air_temp_k)
iq$reanalysis_avg_temp_k <- ifelse(is.na(iq$reanalysis_avg_temp_k), median(iq$reanalysis_avg_temp_k, na.rm=TRUE), iq$reanalysis_avg_temp_k)
iq$reanalysis_dew_point_temp_k <- ifelse(is.na(iq$reanalysis_dew_point_temp_k), median(iq$reanalysis_dew_point_temp_k, na.rm=TRUE), iq$reanalysis_dew_point_temp_k)
iq$reanalysis_max_air_temp_k <- ifelse(is.na(iq$reanalysis_max_air_temp_k), median(iq$reanalysis_max_air_temp_k, na.rm=TRUE), iq$reanalysis_max_air_temp_k)
iq$reanalysis_min_air_temp_k <- ifelse(is.na(iq$reanalysis_min_air_temp_k), median(iq$reanalysis_min_air_temp_k, na.rm=TRUE), iq$reanalysis_min_air_temp_k)
iq$reanalysis_precip_amt_kg_per_m2 <- ifelse(is.na(iq$reanalysis_precip_amt_kg_per_m2), median(iq$reanalysis_precip_amt_kg_per_m2, na.rm=TRUE), iq$reanalysis_precip_amt_kg_per_m2)
iq$reanalysis_relative_humidity_percent <- ifelse(is.na(iq$reanalysis_relative_humidity_percent), median(iq$reanalysis_relative_humidity_percent, na.rm=TRUE), iq$reanalysis_relative_humidity_percent)
iq$reanalysis_sat_precip_amt_mm <- ifelse(is.na(iq$reanalysis_sat_precip_amt_mm), median(iq$reanalysis_sat_precip_amt_mm, na.rm=TRUE), iq$reanalysis_sat_precip_amt_mm)
iq$reanalysis_specific_humidity_g_per_kg<- ifelse(is.na(iq$reanalysis_specific_humidity_g_per_kg), median(iq$reanalysis_specific_humidity_g_per_kg, na.rm=TRUE), iq$reanalysis_specific_humidity_g_per_kg)
iq$reanalysis_tdtr_k<- ifelse(is.na(iq$reanalysis_tdtr_k), median(iq$reanalysis_tdtr_k, na.rm=TRUE), iq$reanalysis_tdtr_k)
iq$station_avg_temp_c<- ifelse(is.na(iq$station_avg_temp_c), median(iq$station_avg_temp_c, na.rm=TRUE), iq$station_avg_temp_c)
iq$station_diur_temp_rng_c<- ifelse(is.na(iq$station_diur_temp_rng_c), median(iq$station_diur_temp_rng_c, na.rm=TRUE), iq$station_diur_temp_rng_c)
iq$station_max_temp_c<- ifelse(is.na(iq$station_max_temp_c), median(iq$station_max_temp_c, na.rm=TRUE), iq$station_max_temp_c)
iq$station_min_temp_c<- ifelse(is.na(iq$station_min_temp_c), median(iq$station_min_temp_c, na.rm=TRUE), iq$station_min_temp_c)
iq$station_precip_mm<- ifelse(is.na(iq$station_precip_mm), median(iq$station_precip_mm, na.rm=TRUE), iq$station_precip_mm)
summary(iq)
## city year weekofyear week_start_date
## Length:520 Min. :2000 Min. : 1.00 Length:520
## Class :character 1st Qu.:2003 1st Qu.:13.75 Class :character
## Mode :character Median :2005 Median :26.50 Mode :character
## Mean :2005 Mean :26.50
## 3rd Qu.:2007 3rd Qu.:39.25
## Max. :2010 Max. :53.00
## total_cases ndvi_ne ndvi_nw ndvi_se
## Min. : 0.000 Min. :0.06173 Min. :0.03586 Min. :0.02988
## 1st Qu.: 1.000 1st Qu.:0.20015 1st Qu.:0.18026 1st Qu.:0.19475
## Median : 5.000 Median :0.26364 Median :0.23297 Median :0.24980
## Mean : 7.565 Mean :0.26387 Mean :0.23875 Mean :0.25012
## 3rd Qu.: 9.000 3rd Qu.:0.31962 3rd Qu.:0.29383 3rd Qu.:0.30213
## Max. :116.000 Max. :0.50836 Max. :0.45443 Max. :0.53831
## ndvi_sw precipitation_amt_mm reanalysis_air_temp_k
## Min. :0.06418 Min. : 0.00 Min. :294.6
## 1st Qu.:0.20430 1st Qu.: 39.15 1st Qu.:297.1
## Median :0.26214 Median : 60.47 Median :297.8
## Mean :0.26675 Mean : 64.22 Mean :297.9
## 3rd Qu.:0.32488 3rd Qu.: 85.64 3rd Qu.:298.6
## Max. :0.54602 Max. :210.83 Max. :301.6
## reanalysis_avg_temp_k reanalysis_dew_point_temp_k
## Min. :294.9 Min. :290.1
## 1st Qu.:298.2 1st Qu.:294.6
## Median :299.1 Median :295.9
## Mean :299.1 Mean :295.5
## 3rd Qu.:300.1 3rd Qu.:296.5
## Max. :302.9 Max. :298.4
## reanalysis_max_air_temp_k reanalysis_min_air_temp_k
## Min. :300.0 Min. :286.9
## 1st Qu.:305.2 1st Qu.:292.0
## Median :307.1 Median :293.1
## Mean :307.1 Mean :292.9
## 3rd Qu.:308.7 3rd Qu.:294.1
## Max. :314.0 Max. :296.0
## reanalysis_precip_amt_kg_per_m2 reanalysis_relative_humidity_percent
## Min. : 0.00 Min. :57.79
## 1st Qu.: 24.21 1st Qu.:84.39
## Median : 46.44 Median :90.92
## Mean : 57.52 Mean :88.66
## 3rd Qu.: 70.43 3rd Qu.:94.55
## Max. :362.03 Max. :98.61
## reanalysis_sat_precip_amt_mm reanalysis_specific_humidity_g_per_kg
## Min. : 0.00 Min. :12.11
## 1st Qu.: 39.15 1st Qu.:16.12
## Median : 60.47 Median :17.43
## Mean : 64.22 Mean :17.10
## 3rd Qu.: 85.64 3rd Qu.:18.18
## Max. :210.83 Max. :20.46
## reanalysis_tdtr_k station_avg_temp_c station_diur_temp_rng_c
## Min. : 3.714 Min. :21.40 Min. : 5.20
## 1st Qu.: 7.371 1st Qu.:27.05 1st Qu.: 9.55
## Median : 8.964 Median :27.60 Median :10.62
## Mean : 9.205 Mean :27.54 Mean :10.57
## 3rd Qu.:11.004 3rd Qu.:28.04 3rd Qu.:11.60
## Max. :16.029 Max. :30.80 Max. :15.80
## station_max_temp_c station_min_temp_c station_precip_mm
## Min. :30.1 Min. :14.7 Min. : 0.00
## 1st Qu.:33.2 1st Qu.:20.6 1st Qu.: 18.00
## Median :34.0 Median :21.3 Median : 45.30
## Mean :34.0 Mean :21.2 Mean : 61.94
## 3rd Qu.:34.8 3rd Qu.:22.0 3rd Qu.: 83.35
## Max. :42.2 Max. :24.2 Max. :543.30
#Test: make NA's Median of variable
iq.test$ndvi_ne <- ifelse(is.na(iq.test$ndvi_ne), median(iq.test$ndvi_ne, na.rm=TRUE), iq.test$ndvi_ne)
iq.test$ndvi_nw <- ifelse(is.na(iq.test$ndvi_nw), median(iq.test$ndvi_nw, na.rm=TRUE), iq.test$ndvi_nw)
iq.test$ndvi_se <- ifelse(is.na(iq.test$ndvi_se ), median(iq.test$ndvi_se , na.rm=TRUE), iq.test$ndvi_se )
iq.test$ndvi_sw <- ifelse(is.na(iq.test$ndvi_sw ), median(iq.test$ndvi_sw , na.rm=TRUE), iq.test$ndvi_sw )
iq.test$precipitation_amt_mm <- ifelse(is.na(iq.test$precipitation_amt_mm), median(iq.test$precipitation_amt_mm, na.rm=TRUE), iq.test$precipitation_amt_mm)
iq.test$reanalysis_air_temp_k <- ifelse(is.na(iq.test$reanalysis_air_temp_k), median(iq.test$reanalysis_air_temp_k, na.rm=TRUE), iq.test$reanalysis_air_temp_k)
iq.test$reanalysis_avg_temp_k <- ifelse(is.na(iq.test$reanalysis_avg_temp_k), median(iq.test$reanalysis_avg_temp_k, na.rm=TRUE), iq.test$reanalysis_avg_temp_k)
iq.test$reanalysis_dew_point_temp_k <- ifelse(is.na(iq.test$reanalysis_dew_point_temp_k), median(iq.test$reanalysis_dew_point_temp_k, na.rm=TRUE), iq.test$reanalysis_dew_point_temp_k)
iq.test$reanalysis_max_air_temp_k <- ifelse(is.na(iq.test$reanalysis_max_air_temp_k), median(iq.test$reanalysis_max_air_temp_k, na.rm=TRUE), iq.test$reanalysis_max_air_temp_k)
iq.test$reanalysis_min_air_temp_k <- ifelse(is.na(iq.test$reanalysis_min_air_temp_k), median(iq.test$reanalysis_min_air_temp_k, na.rm=TRUE), iq.test$reanalysis_min_air_temp_k)
iq.test$reanalysis_precip_amt_kg_per_m2 <- ifelse(is.na(iq.test$reanalysis_precip_amt_kg_per_m2), median(iq.test$reanalysis_precip_amt_kg_per_m2, na.rm=TRUE), iq.test$reanalysis_precip_amt_kg_per_m2)
iq.test$reanalysis_relative_humidity_percent <- ifelse(is.na(iq.test$reanalysis_relative_humidity_percent), median(iq.test$reanalysis_relative_humidity_percent, na.rm=TRUE), iq.test$reanalysis_relative_humidity_percent)
iq.test$reanalysis_sat_precip_amt_mm <- ifelse(is.na(iq.test$reanalysis_sat_precip_amt_mm), median(iq.test$reanalysis_sat_precip_amt_mm, na.rm=TRUE), iq.test$reanalysis_sat_precip_amt_mm)
iq.test$reanalysis_specific_humidity_g_per_kg<- ifelse(is.na(iq.test$reanalysis_specific_humidity_g_per_kg), median(iq.test$reanalysis_specific_humidity_g_per_kg, na.rm=TRUE), iq.test$reanalysis_specific_humidity_g_per_kg)
iq.test$reanalysis_tdtr_k<- ifelse(is.na(iq.test$reanalysis_tdtr_k), median(iq.test$reanalysis_tdtr_k, na.rm=TRUE), iq.test$reanalysis_tdtr_k)
iq.test$station_avg_temp_c<- ifelse(is.na(iq.test$station_avg_temp_c), median(iq.test$station_avg_temp_c, na.rm=TRUE), iq.test$station_avg_temp_c)
iq.test$station_diur_temp_rng_c<- ifelse(is.na(iq.test$station_diur_temp_rng_c), median(iq.test$station_diur_temp_rng_c, na.rm=TRUE), iq.test$station_diur_temp_rng_c)
iq.test$station_max_temp_c<- ifelse(is.na(iq.test$station_max_temp_c), median(iq.test$station_max_temp_c, na.rm=TRUE), iq.test$station_max_temp_c)
iq.test$station_min_temp_c<- ifelse(is.na(iq.test$station_min_temp_c), median(iq.test$station_min_temp_c, na.rm=TRUE), iq.test$station_min_temp_c)
iq.test$station_precip_mm<- ifelse(is.na(iq.test$station_precip_mm), median(iq.test$station_precip_mm, na.rm=TRUE), iq.test$station_precip_mm)
summary(iq.test)
## city year weekofyear week_start_date
## Length:156 Min. :2010 Min. : 1.00 Length:156
## Class :character 1st Qu.:2011 1st Qu.:13.75 Class :character
## Mode :character Median :2012 Median :26.00 Mode :character
## Mean :2012 Mean :26.33
## 3rd Qu.:2012 3rd Qu.:39.00
## Max. :2013 Max. :52.00
## ndvi_ne ndvi_nw ndvi_se ndvi_sw
## Min. :0.08929 Min. :0.06321 Min. :0.09826 Min. :0.08196
## 1st Qu.:0.21496 1st Qu.:0.22244 1st Qu.:0.21198 1st Qu.:0.21750
## Median :0.26523 Median :0.26946 Median :0.25316 Median :0.28153
## Mean :0.26689 Mean :0.27057 Mean :0.25858 Mean :0.28223
## 3rd Qu.:0.31924 3rd Qu.:0.32456 3rd Qu.:0.30164 3rd Qu.:0.34724
## Max. :0.42999 Max. :0.46480 Max. :0.45304 Max. :0.52904
## precipitation_amt_mm reanalysis_air_temp_k reanalysis_avg_temp_k
## Min. : 2.28 Min. :294.6 Min. :295.2
## 1st Qu.: 36.95 1st Qu.:297.1 1st Qu.:298.2
## Median : 51.29 Median :297.8 Median :299.0
## Mean : 57.92 Mean :297.8 Mean :299.0
## 3rd Qu.: 72.81 3rd Qu.:298.3 3rd Qu.:299.8
## Max. :152.32 Max. :301.9 Max. :303.3
## reanalysis_dew_point_temp_k reanalysis_max_air_temp_k
## Min. :292.0 Min. :302.8
## 1st Qu.:294.8 1st Qu.:305.4
## Median :295.9 Median :306.7
## Mean :295.6 Mean :307.0
## 3rd Qu.:296.5 3rd Qu.:308.5
## Max. :297.7 Max. :314.1
## reanalysis_min_air_temp_k reanalysis_precip_amt_kg_per_m2
## Min. :286.2 Min. : 2.60
## 1st Qu.:291.9 1st Qu.: 35.62
## Median :292.9 Median : 59.30
## Mean :292.7 Mean : 72.61
## 3rd Qu.:293.8 3rd Qu.: 98.97
## Max. :296.0 Max. :280.42
## reanalysis_relative_humidity_percent reanalysis_sat_precip_amt_mm
## Min. :66.31 Min. : 2.28
## 1st Qu.:86.32 1st Qu.: 36.95
## Median :91.41 Median : 51.29
## Mean :89.61 Mean : 57.92
## 3rd Qu.:94.74 3rd Qu.: 72.81
## Max. :97.98 Max. :152.32
## reanalysis_specific_humidity_g_per_kg reanalysis_tdtr_k
## Min. :13.74 Min. : 4.800
## 1st Qu.:16.33 1st Qu.: 7.689
## Median :17.55 Median : 9.371
## Mean :17.21 Mean : 9.321
## 3rd Qu.:18.17 3rd Qu.:10.764
## Max. :19.60 Max. :14.486
## station_avg_temp_c station_diur_temp_rng_c station_max_temp_c
## Min. :24.84 Min. : 6.45 Min. :29.60
## 1st Qu.:27.05 1st Qu.: 9.70 1st Qu.:33.20
## Median :27.51 Median :10.73 Median :34.00
## Mean :27.54 Mean :10.74 Mean :33.96
## 3rd Qu.:28.01 3rd Qu.:11.63 3rd Qu.:34.83
## Max. :29.13 Max. :14.72 Max. :38.40
## station_min_temp_c station_precip_mm
## Min. :14.20 Min. : 0.00
## 1st Qu.:20.60 1st Qu.: 11.72
## Median :21.20 Median : 27.20
## Mean :21.09 Mean : 34.25
## 3rd Qu.:21.85 3rd Qu.: 43.62
## Max. :23.20 Max. :212.00
#Plots
attach(iq)
## The following objects are masked from sj:
##
## city, ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw,
## precipitation_amt_mm, reanalysis_air_temp_k,
## reanalysis_avg_temp_k, reanalysis_dew_point_temp_k,
## reanalysis_max_air_temp_k, reanalysis_min_air_temp_k,
## reanalysis_precip_amt_kg_per_m2,
## reanalysis_relative_humidity_percent,
## reanalysis_sat_precip_amt_mm,
## reanalysis_specific_humidity_g_per_kg, reanalysis_tdtr_k,
## station_avg_temp_c, station_diur_temp_rng_c,
## station_max_temp_c, station_min_temp_c, station_precip_mm,
## total_cases, week_start_date, weekofyear, year
plot(ndvi_ne, total_cases)
plot( ndvi_nw, total_cases)
plot(ndvi_se, total_cases)
plot(ndvi_sw, total_cases)
plot(precipitation_amt_mm, total_cases)
plot(reanalysis_air_temp_k,total_cases)
plot(reanalysis_dew_point_temp_k,total_cases)
plot(reanalysis_max_air_temp_k,total_cases)
plot(reanalysis_min_air_temp_k,total_cases)
plot(reanalysis_precip_amt_kg_per_m2,total_cases)
plot(reanalysis_relative_humidity_percent,total_cases)
plot(reanalysis_sat_precip_amt_mm,total_cases)
plot(reanalysis_specific_humidity_g_per_kg,total_cases)
plot(reanalysis_tdtr_k,total_cases)
plot(station_avg_temp_c,total_cases)
plot(station_diur_temp_rng_c,total_cases)
plot(station_max_temp_c,total_cases)
plot(station_min_temp_c,total_cases)
plot(station_precip_mm,total_cases)
###################
par(mfrow=c(1,1))
plot(reanalysis_specific_humidity_g_per_kg,total_cases, ylab= "Total Cases", xlab = "Mean Specific Humidity", main = "Iquitos, Peru")#mean specific humidity
plot(reanalysis_min_air_temp_k,total_cases, ylab= "Total Cases", xlab = "Minimum Air Temperature" )#minimum air temperature
plot(reanalysis_dew_point_temp_k ,total_cases, ylab= "Total Cases", xlab = "Mean Dew Point Temperature" )#mean dew point temperature
#Correlation
mycor_iq=cor(iq[,5:25])
corrplot(mycor_iq, type = "lower", order = "original",
tl.col = "black", tl.cex=0.70, tl.srt = 10)
#Models
##Regular
iqtot=ts(iq$total_cases, frequency=52, start=c(2000,07,01))
acf(iqtot)
pacf(iqtot)
plot(iqtot, ylab= "Total Cases", main="Iquitos, Peru")
acf(iqtot, main="ACF: Iquitos, Peru")
pacf(iqtot, main="PACF: Iquitos, Peru")
xregg1<-cbind(iq$precipitation_amt_mm, iq$station_avg_temp_c)
xregg1.test<-cbind(iq.test$precipitation_amt_mm, iq.test$station_avg_temp_c)
#test set up
testset ="https://s3.amazonaws.com/drivendata/data/44/public/submission_format.csv"
x = read.csv(file=testset)
###############################################################
#SJ#ARIMA w/Xreg
fit.xreg = auto.arima(sjtot, xreg = xreg1, seasonal=TRUE)
fit.xreg
## Series: sjtot
## Regression with ARIMA(1,1,1) errors
##
## Coefficients:
## ar1 ma1 xreg1 xreg2
## 0.7106 -0.5929 0.0011 1.1431
## s.e. 0.0953 0.1082 0.0074 0.6134
##
## sigma^2 estimated as 180.6: log likelihood=-3754.07
## AIC=7518.14 AICc=7518.2 BIC=7542.34
SJ_xreg_fcast=forecast(fit.xreg,xreg = xreg1.test, h=260)
plot(SJ_xreg_fcast, xlab= "Time", ylab= "Total Cases")
#IQ#ARIMA w/Xreg
iq.fit.xreg = auto.arima(iqtot, xreg = xregg1, seasonal=TRUE)
iq.fit.xreg
## Series: iqtot
## Regression with ARIMA(0,1,2)(0,0,1)[52] errors
##
## Coefficients:
## ma1 ma2 sma1 xreg1 xreg2
## -0.2509 -0.2308 0.0573 -0.0012 -0.1445
## s.e. 0.0430 0.0449 0.0375 0.0080 0.3575
##
## sigma^2 estimated as 52.31: log likelihood=-1761
## AIC=3533.99 AICc=3534.16 BIC=3559.5
IQ_xreg_fcast=forecast(iq.fit.xreg, xreg = xregg1.test ,h=156)
IQ_xreg_fcast$mean
## Time Series:
## Start = c(2010, 7)
## End = c(2013, 6)
## Frequency = 52
## [1] 3.776943 3.587273 3.428449 3.460916 3.588377 3.672421 3.853572
## [8] 3.843287 3.731227 3.359088 3.522173 3.378328 3.116142 3.247158
## [15] 3.125092 3.085333 3.328766 3.101875 3.318532 3.649932 3.628971
## [22] 3.661095 3.431634 3.569505 3.281089 3.312096 3.476700 3.936239
## [29] 3.817860 4.253785 4.198785 3.846642 4.082058 4.396595 4.111886
## [36] 4.065611 4.091741 4.364676 3.813886 3.656966 3.816574 3.885558
## [43] 3.799337 3.468457 3.417117 3.945154 3.669459 3.598197 3.854891
## [50] 3.273418 3.396375 3.588406 3.601376 3.593752 3.608601 3.640733
## [57] 3.613257 3.625361 3.694636 3.552703 3.423451 3.510059 3.562308
## [64] 3.553830 3.542688 3.666346 3.553240 3.544307 3.356753 3.627941
## [71] 3.421670 3.369573 3.539126 3.558536 3.420364 3.428474 3.615156
## [78] 3.474770 3.385794 3.564079 3.549591 3.456313 3.615547 3.613948
## [85] 3.696206 3.538394 3.432147 3.612706 3.562551 3.611507 3.592708
## [92] 3.404298 3.456619 3.369686 3.520241 3.526256 3.547867 3.664950
## [99] 3.592148 3.599259 3.647832 3.666929 3.949030 3.628597 3.597659
## [106] 3.849115 3.742503 3.787026 3.936695 3.732497 3.686214 3.618256
## [113] 3.764713 3.571602 3.624946 3.405887 3.539434 3.501788 3.616290
## [120] 3.408230 3.498537 3.582168 3.391498 3.386556 3.579289 3.698263
## [127] 3.501738 3.431612 3.452084 3.465577 3.449452 3.744818 3.695136
## [134] 3.445763 3.695746 3.389694 3.430022 3.606474 3.571092 3.462254
## [141] 3.475223 3.480811 3.410288 3.308220 3.505382 3.458750 3.625310
## [148] 3.667651 3.419193 3.470747 3.650867 3.597590 3.542378 3.579353
## [155] 3.700717 3.591511
plot(IQ_xreg_fcast, xlab= "Time", ylab= "Total Cases")
#Model 1: Submission
par(mfrow=c(1,2))
plot(SJ_xreg_fcast, xlab= "Time", ylab= "Total Cases")
plot(IQ_xreg_fcast, xlab= "Time", ylab= "Total Cases")
x$total_cases[1:260]<-round(SJ_xreg_fcast$mean)
x$total_cases[261:416]<-round(IQ_xreg_fcast$mean)
write.csv(x, file="~/Desktop/sub..1.csv", row.names = FALSE)
#SCORE=34.0841
###############################################################
#SJ#Neural Net w/ xreg
set.seed(1)
SJ_nnetarxreg_fit=nnetar(sjtot, xreg = xreg1)
summary(SJ_nnetarxreg_fit)
## Length Class Mode
## x 936 ts numeric
## m 1 -none- numeric
## p 1 -none- numeric
## P 1 -none- numeric
## scalex 2 -none- list
## scalexreg 2 -none- list
## size 1 -none- numeric
## xreg 1872 -none- numeric
## subset 936 -none- numeric
## model 20 nnetarmodels list
## nnetargs 0 -none- list
## fitted 936 ts numeric
## residuals 936 ts numeric
## lags 14 -none- numeric
## series 1 -none- character
## method 1 -none- character
## call 3 -none- call
SJ_nnetarxreg_fcast=forecast(SJ_nnetarxreg_fit,xreg = xreg1.test, h=260)
summary(SJ_nnetarxreg_fcast)
##
## Forecast method: NNAR(13,1,8)[52]
##
## Model Information:
##
## Average of 20 networks, each of which is
## a 16-8-1 network with 145 weights
## options were - linear output units
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.01425697 6.954031 5.036321 -Inf Inf 0.1378575 -0.03078745
##
## Forecasts:
## Point Forecast
## 2008.058 5.088645
## 2008.077 3.847115
## 2008.096 4.752338
## 2008.115 6.043877
## 2008.135 6.611736
## 2008.154 7.710873
## 2008.173 7.472490
## 2008.192 8.828239
## 2008.212 10.274801
## 2008.231 10.922776
## 2008.250 10.538671
## 2008.269 11.940997
## 2008.288 13.078818
## 2008.308 14.934234
## 2008.327 15.487494
## 2008.346 17.567950
## 2008.365 18.890467
## 2008.385 20.460546
## 2008.404 24.083159
## 2008.423 23.989087
## 2008.442 24.505618
## 2008.462 23.911623
## 2008.481 27.149547
## 2008.500 27.728051
## 2008.519 26.483678
## 2008.538 26.427277
## 2008.558 26.458758
## 2008.577 26.641633
## 2008.596 26.343511
## 2008.615 24.402585
## 2008.635 22.376130
## 2008.654 20.904338
## 2008.673 19.927544
## 2008.692 18.430392
## 2008.712 16.960008
## 2008.731 16.621653
## 2008.750 14.684839
## 2008.769 13.607102
## 2008.788 12.519399
## 2008.808 11.286424
## 2008.827 10.543947
## 2008.846 9.808920
## 2008.865 9.123115
## 2008.885 8.473608
## 2008.904 8.175295
## 2008.923 7.576489
## 2008.942 7.300688
## 2008.962 6.922615
## 2008.981 6.693975
## 2009.000 6.128399
## 2009.019 5.388297
## 2009.038 5.136742
## 2009.058 4.822114
## 2009.077 4.297264
## 2009.096 4.320100
## 2009.115 3.862846
## 2009.135 5.805671
## 2009.154 5.647684
## 2009.173 6.085603
## 2009.192 6.822438
## 2009.212 7.847304
## 2009.231 11.094361
## 2009.250 12.279047
## 2009.269 14.040638
## 2009.288 16.084920
## 2009.308 18.424402
## 2009.327 21.246039
## 2009.346 22.724605
## 2009.365 25.823715
## 2009.385 30.533808
## 2009.404 35.664473
## 2009.423 38.746508
## 2009.442 44.783837
## 2009.462 49.552103
## 2009.481 60.056008
## 2009.500 73.639911
## 2009.519 87.642760
## 2009.538 100.213967
## 2009.558 117.325103
## 2009.577 129.994651
## 2009.596 142.603422
## 2009.615 160.208747
## 2009.635 191.904123
## 2009.654 251.234288
## 2009.673 326.137134
## 2009.692 371.056102
## 2009.712 406.081647
## 2009.731 411.630302
## 2009.750 424.888406
## 2009.769 423.203796
## 2009.788 414.099044
## 2009.808 402.210430
## 2009.827 404.377575
## 2009.846 396.874093
## 2009.865 368.563320
## 2009.885 297.761704
## 2009.904 234.504465
## 2009.923 187.861682
## 2009.942 149.033630
## 2009.962 120.447636
## 2009.981 100.054105
## 2010.000 79.689401
## 2010.019 52.749683
## 2010.038 39.308540
## 2010.058 34.936264
## 2010.077 29.604339
## 2010.096 21.012707
## 2010.115 21.349653
## 2010.135 23.392616
## 2010.154 23.659440
## 2010.173 22.632830
## 2010.192 19.578691
## 2010.212 19.639000
## 2010.231 20.948913
## 2010.250 25.357251
## 2010.269 28.188724
## 2010.288 28.109095
## 2010.308 27.959239
## 2010.327 32.802292
## 2010.346 37.943803
## 2010.365 40.991283
## 2010.385 47.468744
## 2010.404 54.119953
## 2010.423 62.560400
## 2010.442 66.827356
## 2010.462 71.576278
## 2010.481 72.837708
## 2010.500 75.897677
## 2010.519 77.884278
## 2010.538 78.062825
## 2010.558 74.253207
## 2010.577 70.779672
## 2010.596 65.930466
## 2010.615 58.762930
## 2010.635 50.865548
## 2010.654 44.796036
## 2010.673 41.135459
## 2010.692 38.066861
## 2010.712 35.613727
## 2010.731 33.630944
## 2010.750 32.583358
## 2010.769 31.628132
## 2010.788 31.722848
## 2010.808 31.448876
## 2010.827 31.429510
## 2010.846 31.498106
## 2010.865 31.051469
## 2010.885 30.078202
## 2010.904 28.102563
## 2010.923 26.129542
## 2010.942 24.491667
## 2010.962 23.202088
## 2010.981 21.926408
## 2011.000 20.805006
## 2011.019 19.337563
## 2011.038 17.804681
## 2011.058 16.426404
## 2011.077 15.343892
## 2011.096 14.117980
## 2011.115 14.064858
## 2011.135 12.910478
## 2011.154 13.373484
## 2011.173 12.384276
## 2011.192 13.128529
## 2011.212 13.481649
## 2011.231 14.864967
## 2011.250 14.723598
## 2011.269 14.981077
## 2011.288 14.544481
## 2011.308 16.576989
## 2011.327 16.343018
## 2011.346 18.195440
## 2011.365 18.807441
## 2011.385 20.331291
## 2011.404 20.246653
## 2011.423 21.795483
## 2011.442 23.481510
## 2011.462 21.391377
## 2011.481 23.413914
## 2011.500 25.638359
## 2011.519 25.671286
## 2011.538 26.321917
## 2011.558 24.770866
## 2011.577 25.395322
## 2011.596 25.406219
## 2011.615 24.919911
## 2011.635 23.690998
## 2011.654 21.727814
## 2011.673 20.678636
## 2011.692 21.030636
## 2011.712 19.175935
## 2011.731 17.612463
## 2011.750 16.234265
## 2011.769 15.132072
## 2011.788 14.341213
## 2011.808 13.354365
## 2011.827 12.292077
## 2011.846 11.315559
## 2011.865 10.733464
## 2011.885 10.166181
## 2011.904 9.153495
## 2011.923 8.589851
## 2011.942 8.162125
## 2011.962 8.167209
## 2011.981 8.130879
## 2012.000 7.473669
## 2012.019 6.712765
## 2012.038 6.179551
## 2012.058 5.587431
## 2012.077 6.522614
## 2012.096 5.819315
## 2012.115 6.700274
## 2012.135 6.496977
## 2012.154 6.424659
## 2012.173 8.688106
## 2012.192 10.548344
## 2012.212 13.820543
## 2012.231 16.766604
## 2012.250 18.316553
## 2012.269 18.902166
## 2012.288 21.499214
## 2012.308 23.295703
## 2012.327 26.745613
## 2012.346 29.163360
## 2012.365 32.727514
## 2012.385 38.237474
## 2012.404 39.492147
## 2012.423 44.605467
## 2012.442 54.066403
## 2012.462 65.800197
## 2012.481 82.009021
## 2012.500 91.588951
## 2012.519 103.091932
## 2012.538 118.303437
## 2012.558 135.087178
## 2012.577 159.864546
## 2012.596 188.355393
## 2012.615 230.008428
## 2012.635 293.583774
## 2012.654 353.877449
## 2012.673 403.128021
## 2012.692 415.102335
## 2012.712 419.543616
## 2012.731 420.954115
## 2012.750 422.051344
## 2012.769 414.240250
## 2012.788 408.428342
## 2012.808 400.449445
## 2012.827 390.893099
## 2012.846 332.087358
## 2012.865 268.605505
## 2012.885 220.708524
## 2012.904 170.516963
## 2012.923 148.549477
## 2012.942 119.122117
## 2012.962 97.068677
## 2012.981 73.671054
## 2013.000 53.114647
## 2013.019 42.955948
## 2013.038 33.866597
plot(SJ_nnetarxreg_fcast,xlab= "Time", ylab= "Total Cases")
#IQ#Neural Net w/ xreg
set.seed(1)
IQ_nnetarxreg_fit=nnetar(iqtot, xreg = xregg1)
summary(IQ_nnetarxreg_fit)
## Length Class Mode
## x 520 ts numeric
## m 1 -none- numeric
## p 1 -none- numeric
## P 1 -none- numeric
## scalex 2 -none- list
## scalexreg 2 -none- list
## size 1 -none- numeric
## xreg 1040 -none- numeric
## subset 520 -none- numeric
## model 20 nnetarmodels list
## nnetargs 0 -none- list
## fitted 520 ts numeric
## residuals 520 ts numeric
## lags 6 -none- numeric
## series 1 -none- character
## method 1 -none- character
## call 3 -none- call
IQ_nnxreg_fcast=forecast(IQ_nnetarxreg_fit,xreg = xregg1.test, h=156)
summary(IQ_nnxreg_fcast)
##
## Forecast method: NNAR(5,1,4)[52]
##
## Model Information:
##
## Average of 20 networks, each of which is
## a 8-4-1 network with 41 weights
## options were - linear output units
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -0.003093782 4.446992 3.093393 -Inf Inf 0.327684 0.02708984
##
## Forecasts:
## Point Forecast
## 2010.115 4.038533
## 2010.135 3.623803
## 2010.154 3.983151
## 2010.173 4.205464
## 2010.192 4.097784
## 2010.212 4.811998
## 2010.231 4.461265
## 2010.250 4.741493
## 2010.269 4.844160
## 2010.288 5.347565
## 2010.308 4.918594
## 2010.327 5.805969
## 2010.346 5.995956
## 2010.365 6.277155
## 2010.385 6.874579
## 2010.404 7.494688
## 2010.423 7.148932
## 2010.442 7.825965
## 2010.462 7.707098
## 2010.481 7.830498
## 2010.500 7.097768
## 2010.519 6.844158
## 2010.538 7.084950
## 2010.558 6.939062
## 2010.577 7.326344
## 2010.596 7.760601
## 2010.615 6.717712
## 2010.635 6.985027
## 2010.654 6.897127
## 2010.673 7.030315
## 2010.692 6.550053
## 2010.712 6.724809
## 2010.731 7.005525
## 2010.750 6.993938
## 2010.769 6.923062
## 2010.788 6.744460
## 2010.808 5.840452
## 2010.827 6.371155
## 2010.846 6.088917
## 2010.865 5.653397
## 2010.885 5.737285
## 2010.904 5.153690
## 2010.923 3.961838
## 2010.942 4.088506
## 2010.962 4.359410
## 2010.981 4.238415
## 2011.000 3.812801
## 2011.019 3.541580
## 2011.038 4.601873
## 2011.058 3.964750
## 2011.077 4.311263
## 2011.096 4.291770
## 2011.115 4.543324
## 2011.135 4.531384
## 2011.154 4.709956
## 2011.173 4.237909
## 2011.192 3.897382
## 2011.212 4.422344
## 2011.231 4.346619
## 2011.250 5.025619
## 2011.269 5.345148
## 2011.288 5.648902
## 2011.308 6.152011
## 2011.327 5.756262
## 2011.346 6.031906
## 2011.365 5.428974
## 2011.385 5.714675
## 2011.404 5.741738
## 2011.423 6.123639
## 2011.442 5.804292
## 2011.462 6.427088
## 2011.481 6.733292
## 2011.500 6.633761
## 2011.519 6.178266
## 2011.538 6.410688
## 2011.558 6.640109
## 2011.577 6.069722
## 2011.596 5.531649
## 2011.615 5.395108
## 2011.635 5.817327
## 2011.654 5.352898
## 2011.673 5.011558
## 2011.692 4.785355
## 2011.712 4.770352
## 2011.731 4.696702
## 2011.750 4.347093
## 2011.769 4.270373
## 2011.788 3.693510
## 2011.808 3.323248
## 2011.827 3.503816
## 2011.846 3.565839
## 2011.865 3.305253
## 2011.885 3.100382
## 2011.904 3.775406
## 2011.923 3.629143
## 2011.942 4.079875
## 2011.962 4.085831
## 2011.981 4.096746
## 2012.000 3.818143
## 2012.019 4.193773
## 2012.038 4.212230
## 2012.058 4.315927
## 2012.077 4.111017
## 2012.096 4.360807
## 2012.115 3.977162
## 2012.135 4.354985
## 2012.154 3.907922
## 2012.173 4.197796
## 2012.192 4.174864
## 2012.212 4.185362
## 2012.231 4.227915
## 2012.250 4.592673
## 2012.269 4.329388
## 2012.288 4.823685
## 2012.308 5.201175
## 2012.327 5.399574
## 2012.346 5.635121
## 2012.365 6.004496
## 2012.385 5.445819
## 2012.404 5.794748
## 2012.423 5.935080
## 2012.442 5.472985
## 2012.462 5.946041
## 2012.481 6.117068
## 2012.500 6.052461
## 2012.519 5.452802
## 2012.538 5.973585
## 2012.558 6.070263
## 2012.577 5.265583
## 2012.596 5.573375
## 2012.615 5.789533
## 2012.635 5.243074
## 2012.654 4.984141
## 2012.673 4.678836
## 2012.692 4.631255
## 2012.712 4.531955
## 2012.731 4.234308
## 2012.750 4.152864
## 2012.769 4.467492
## 2012.788 4.035023
## 2012.808 4.780926
## 2012.827 4.482894
## 2012.846 4.061019
## 2012.865 5.163101
## 2012.885 4.777378
## 2012.904 5.066142
## 2012.923 4.468692
## 2012.942 4.818436
## 2012.962 4.915486
## 2012.981 5.170873
## 2013.000 4.460840
## 2013.019 4.778718
## 2013.038 4.573304
## 2013.058 4.329047
## 2013.077 4.256575
## 2013.096 4.387227
plot(IQ_nnxreg_fcast,xlab= "Time", ylab= "Total Cases")
#Model 2: Submission
par(mfrow=c(1,2))
plot(SJ_nnetarxreg_fcast,xlab= "Time", ylab= "Total Cases")
plot(IQ_nnxreg_fcast,xlab= "Time", ylab= "Total Cases")
x2$total_cases[1:260]<-round(SJ_nnetarxreg_fcast$mean)
x2$total_cases[261:416]<-round(IQ_nnxreg_fcast$mean)
write.csv(x2, file="~/Desktop/sub..2.csv", row.names = FALSE)
#SCORE= 36.4087
###############################################################
#SJ#Stlf, Forecasting using stl objects
SJ_ets<-stlf(sjtot, method="ets", h=260)
summary(SJ_ets)
##
## Forecast method: STL + ETS(A,Ad,N)
##
## Model Information:
## ETS(A,Ad,N)
##
## Call:
## ets(y = x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend)
##
## Smoothing parameters:
## alpha = 0.9998
## beta = 0.1206
## phi = 0.8
##
## Initial states:
## l = 29.3372
## b = 0.07
##
## sigma: 10.9579
##
## AIC AICc BIC
## 10897.44 10897.53 10926.49
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.0002649299 10.95795 7.067943 -Inf Inf 0.1934684 0.04828091
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2008.058 12.888537 -1.154638 26.93171 -8.588645 34.36572
## 2008.077 12.844129 -7.993807 33.68206 -19.024744 44.71300
## 2008.096 13.932199 -12.634142 40.49854 -26.697514 54.56191
## 2008.115 12.849703 -18.877986 44.57739 -35.673610 61.37302
## 2008.135 19.181395 -17.316061 55.67885 -36.636647 74.99944
## 2008.154 18.600830 -22.360235 59.56189 -44.043713 81.24537
## 2008.173 19.550751 -25.618464 64.71997 -49.529602 88.63110
## 2008.192 25.273348 -23.883081 74.42978 -49.904922 100.45162
## 2008.212 24.512771 -28.435793 77.46134 -56.465070 105.49061
## 2008.231 24.086494 -32.479771 80.65276 -62.424143 110.59713
## 2008.250 28.900320 -31.126384 88.92702 -62.902602 120.70324
## 2008.269 34.879749 -28.464805 98.22430 -61.997386 131.75688
## 2008.288 36.926085 -29.606478 103.45865 -64.826689 138.67886
## 2008.308 39.178046 -30.423881 108.77997 -67.268916 145.62501
## 2008.327 53.846397 -18.716162 126.40896 -57.128460 164.82125
## 2008.346 51.597735 -23.825535 127.02101 -63.752203 166.94767
## 2008.365 64.014463 -14.177468 142.20639 -55.569776 183.59870
## 2008.385 65.777470 -15.098117 146.65306 -57.911067 189.46601
## 2008.404 64.424708 -19.055856 147.90527 -63.247797 192.09721
## 2008.423 75.420705 -10.591847 161.43326 -56.124141 206.96555
## 2008.442 67.943637 -20.533042 156.42032 -67.369767 203.25704
## 2008.462 66.954605 -23.922974 157.83218 -72.030658 205.93987
## 2008.481 75.769245 -17.450198 168.98869 -66.797591 218.33608
## 2008.500 59.874243 -35.631831 155.38032 -86.189692 205.93818
## 2008.519 53.976491 -43.764432 151.71741 -95.505351 203.45833
## 2008.538 45.909457 -54.017675 145.83659 -106.915904 198.73482
## 2008.558 41.898915 -60.168653 143.96648 -114.199959 197.99779
## 2008.577 40.118875 -64.045971 144.28372 -119.187510 199.42526
## 2008.596 31.533151 -74.688213 137.75451 -130.918407 193.98471
## 2008.615 29.432555 -78.806765 137.67187 -136.105199 194.97031
## 2008.635 28.413387 -81.807345 138.63412 -140.154675 196.98145
## 2008.654 21.689042 -90.478417 133.85650 -149.856282 193.23437
## 2008.673 21.161950 -92.919265 135.24317 -153.310212 195.63411
## 2008.692 19.357248 -96.606338 135.32083 -157.993752 196.70825
## 2008.712 18.932489 -98.883547 136.74853 -161.251590 199.11657
## 2008.731 18.053619 -101.586309 137.69355 -164.919862 201.02710
## 2008.750 17.771541 -103.664982 139.20806 -167.949595 203.49268
## 2008.769 22.565727 -100.641271 145.77272 -165.863116 210.99457
## 2008.788 17.251893 -107.700554 142.20434 -173.846384 208.35017
## 2008.808 14.972408 -111.701486 141.64630 -178.758595 208.70341
## 2008.827 13.821246 -114.551050 142.19354 -182.507238 210.14973
## 2008.846 14.890513 -115.158034 144.93906 -184.001576 213.78260
## 2008.865 14.254446 -117.449043 145.95793 -187.168658 215.67755
## 2008.885 15.225603 -118.112306 148.56351 -188.697131 219.14834
## 2008.904 11.959819 -122.992731 146.91237 -194.432296 218.35193
## 2008.923 9.414506 -127.133606 145.96262 -199.417810 218.24682
## 2008.942 13.237027 -124.888225 151.36228 -198.007317 224.48137
## 2008.962 9.884537 -129.800056 149.56913 -203.744614 223.51369
## 2008.981 7.660414 -133.566311 148.88714 -208.327223 223.64805
## 2009.000 9.388068 -133.364133 152.14027 -208.932584 227.70872
## 2009.019 7.068636 -137.192915 151.33019 -213.560368 227.69764
## 2009.038 7.101538 -138.653738 152.85681 -215.811920 230.01499
## 2009.058 14.570022 -132.663826 161.80387 -210.604717 239.74476
## 2009.077 14.189317 -134.508404 162.88704 -213.224222 241.60286
## 2009.096 15.008350 -135.138974 165.15567 -214.622166 244.63887
## 2009.115 13.710625 -137.872441 165.29369 -218.115669 245.53692
## 2009.135 19.870132 -133.135204 172.87547 -214.131337 253.87160
## 2009.154 19.151820 -135.262688 173.56633 -217.004792 255.30843
## 2009.173 19.991543 -135.819394 175.80248 -218.300721 258.28381
## 2009.192 25.625982 -131.568979 182.82094 -214.782965 266.03493
## 2009.212 24.794878 -133.772028 183.36178 -217.712277 267.30203
## 2009.231 24.312180 -135.614902 184.23926 -220.275186 268.89955
## 2009.250 29.080868 -132.194919 190.35666 -217.569164 275.73090
## 2009.269 35.024188 -127.589119 197.63750 -213.671404 283.71978
## 2009.288 37.041636 -126.898279 200.98155 -213.682828 287.76610
## 2009.308 39.270487 -125.985387 204.52636 -213.466562 292.00754
## 2009.327 53.920350 -112.641086 220.48179 -200.813384 308.65408
## 2009.346 51.656898 -116.199947 219.51374 -205.057993 308.37179
## 2009.365 64.061793 -105.080539 233.20412 -194.619081 322.74267
## 2009.385 65.815334 -104.602789 236.23346 -194.816694 326.44736
## 2009.404 64.454999 -107.229435 236.13943 -198.113685 327.02368
## 2009.423 75.444938 -97.496535 248.38641 -189.046221 339.93610
## 2009.442 67.963023 -106.226418 242.15246 -198.436738 334.36278
## 2009.462 66.970114 -108.458418 242.39865 -201.324672 335.26490
## 2009.481 75.781653 -100.877278 252.44058 -194.394867 345.95817
## 2009.500 59.884169 -117.996651 237.76499 -212.161069 331.92941
## 2009.519 53.984432 -125.109941 233.07880 -219.916776 327.88564
## 2009.538 45.915809 -134.383949 226.21557 -229.828875 321.66049
## 2009.558 41.903997 -139.593141 223.40113 -235.671922 319.47992
## 2009.577 40.122940 -142.563729 222.80961 -239.272210 319.51809
## 2009.596 31.536403 -152.332103 215.40491 -249.666210 312.73902
## 2009.615 29.435157 -155.607638 214.47795 -253.563376 312.43369
## 2009.635 28.415468 -157.794209 214.62515 -256.367659 313.19860
## 2009.654 21.690707 -165.678587 209.06000 -264.865900 308.24731
## 2009.673 21.163282 -167.358495 209.68506 -267.155896 309.48246
## 2009.692 19.358314 -170.308945 209.02557 -270.712726 309.42935
## 2009.712 18.933342 -171.872520 209.73920 -272.879043 310.74573
## 2009.731 18.054301 -173.883411 209.99201 -275.489099 311.59770
## 2009.750 17.772087 -175.290840 210.83501 -277.492180 313.03635
## 2009.769 22.566164 -171.615458 216.74778 -274.408999 319.54133
## 2009.788 17.252242 -178.041665 212.54615 -281.424015 315.92850
## 2009.808 14.972688 -181.427207 211.37258 -285.395031 315.34041
## 2009.827 13.821469 -183.678219 211.32116 -288.228239 315.87118
## 2009.846 14.890692 -183.702699 213.48408 -288.831691 318.61307
## 2009.865 14.254589 -185.426516 213.93569 -291.131307 319.64048
## 2009.885 15.225717 -185.537207 215.98864 -291.814679 322.26611
## 2009.904 11.959911 -189.879035 213.79886 -296.726118 320.64594
## 2009.923 9.414579 -193.494682 212.32384 -300.908357 319.73751
## 2009.942 13.237085 -190.736875 217.21105 -298.714167 325.18834
## 2009.962 9.884584 -195.148547 214.91772 -303.686530 323.45570
## 2009.981 7.660451 -198.426407 213.74731 -307.522200 322.84310
## 2010.000 9.388098 -197.747127 216.52332 -307.397891 326.17409
## 2010.019 7.068660 -201.109652 215.24697 -311.312594 325.44991
## 2010.038 7.101557 -202.114642 216.31776 -312.867008 327.07012
## 2010.058 14.570038 -195.678925 224.81900 -306.978002 336.11808
## 2010.077 14.189330 -197.087348 225.46601 -308.930465 337.30912
## 2010.096 15.008360 -197.291058 227.30778 -309.675581 339.69230
## 2010.115 13.710633 -199.606622 227.02789 -312.529955 339.95122
## 2010.135 19.870139 -194.460119 234.20040 -307.919704 347.65998
## 2010.154 19.151825 -196.186670 234.49032 -310.179984 348.48363
## 2010.173 19.991547 -196.350487 236.33358 -310.875043 350.85814
## 2010.192 25.625985 -191.714955 242.96693 -306.768298 358.02027
## 2010.212 24.794881 -193.540394 243.13016 -309.120108 358.70987
## 2010.231 24.312182 -195.012921 243.63728 -311.116617 359.74098
## 2010.250 29.080870 -191.229613 249.39135 -307.854938 366.01668
## 2010.269 35.024190 -186.267286 256.31567 -303.411918 373.46030
## 2010.288 37.041637 -185.226502 259.30978 -302.888147 376.97142
## 2010.308 39.270488 -183.970041 262.51102 -302.146439 380.68741
## 2010.327 53.920351 -170.288351 278.12905 -288.977269 396.81797
## 2010.346 51.656898 -173.515814 276.82961 -292.715048 396.02884
## 2010.365 64.061793 -162.070820 290.19441 -281.778194 409.90178
## 2010.385 65.815334 -161.273122 292.90379 -281.486489 413.11716
## 2010.404 64.454999 -163.585294 292.49529 -284.302533 413.21253
## 2010.423 75.444938 -153.543235 304.43311 -274.762252 425.65213
## 2010.442 67.963023 -161.969123 297.89517 -283.687849 419.61390
## 2010.462 66.970114 -163.902145 297.84237 -286.118537 420.05876
## 2010.481 75.781653 -156.026907 307.59021 -278.738946 430.30225
## 2010.500 59.884169 -172.856924 292.62526 -296.062617 415.83096
## 2010.519 53.984432 -179.685474 287.65434 -303.382850 411.35171
## 2010.538 45.915809 -188.679232 280.51085 -312.866345 404.69796
## 2010.558 41.903997 -193.612545 277.42054 -318.287471 402.09546
## 2010.577 40.122940 -196.311511 276.55739 -321.472349 401.71823
## 2010.596 31.536403 -205.812408 268.88521 -331.457278 394.53009
## 2010.615 29.435157 -208.824505 267.69482 -334.951551 393.82186
## 2010.635 28.415468 -210.751575 267.58251 -337.358960 394.18990
## 2010.654 21.690707 -218.380289 261.76170 -345.466197 388.84761
## 2010.673 21.163282 -219.808274 262.13484 -347.370911 389.69748
## 2010.692 19.358314 -222.510451 261.22708 -350.548041 389.26467
## 2010.712 18.933342 -223.829315 261.69600 -352.340103 390.20679
## 2010.731 18.054301 -225.598969 261.70757 -354.581219 390.68982
## 2010.750 17.772087 -226.768553 262.31273 -356.220548 391.76472
## 2010.769 22.566164 -222.858637 267.99096 -352.778678 397.91101
## 2010.788 17.252242 -229.053545 263.55803 -359.439953 393.94444
## 2010.808 14.972688 -232.210947 262.15632 -363.062059 393.00743
## 2010.827 13.821469 -234.236906 261.87984 -365.551077 393.19402
## 2010.846 14.890692 -234.039350 263.82073 -365.814954 395.59634
## 2010.865 14.254589 -235.544078 264.05326 -367.779505 396.28868
## 2010.885 15.225717 -235.438565 265.89000 -368.132220 398.58365
## 2010.904 11.959911 -239.567007 263.48683 -372.717315 396.63714
## 2010.923 9.414579 -242.972027 261.80118 -376.577426 395.40658
## 2010.942 13.237085 -240.006290 266.48046 -374.065235 400.53941
## 2010.962 9.884584 -244.212671 263.98184 -378.723633 398.49280
## 2010.981 7.660451 -247.287825 262.60873 -382.249290 397.57019
## 2011.000 9.388098 -246.408368 265.18456 -381.818837 400.59503
## 2011.019 7.068660 -249.573192 263.71051 -385.431182 399.56850
## 2011.038 7.101557 -250.382906 264.58602 -386.686947 400.89006
## 2011.058 14.570038 -243.754287 272.89436 -380.502924 409.64300
## 2011.077 14.189330 -244.972136 273.35079 -382.163928 410.54259
## 2011.096 15.008360 -244.987550 275.00427 -382.621071 412.63779
## 2011.115 13.710633 -247.117053 274.53832 -385.190889 412.61215
## 2011.135 19.870139 -241.786678 281.52696 -380.299430 420.03971
## 2011.154 19.151825 -243.331504 281.63515 -382.281785 420.58544
## 2011.173 19.991547 -243.315700 283.29879 -382.702137 422.68523
## 2011.192 25.625985 -238.502610 289.75458 -378.323842 429.57581
## 2011.212 24.794881 -240.152516 289.74228 -380.407195 429.99696
## 2011.231 24.312182 -241.451494 290.07586 -382.138285 430.76265
## 2011.250 29.080870 -237.496585 295.65833 -378.614165 436.77591
## 2011.269 35.024190 -232.364568 302.41295 -373.911627 443.96001
## 2011.288 37.041637 -231.155969 305.23924 -373.131206 447.21448
## 2011.308 39.270488 -229.733535 308.27451 -372.135663 450.67664
## 2011.327 53.920351 -215.887679 323.72838 -358.715422 466.55612
## 2011.346 51.656898 -218.952749 322.26654 -362.204843 465.51864
## 2011.365 64.061793 -207.347104 335.47069 -351.022295 479.14588
## 2011.385 65.815334 -206.390466 338.02113 -350.487512 482.11818
## 2011.404 64.454999 -208.545378 337.45538 -353.063048 481.97305
## 2011.423 75.444938 -198.347710 349.23759 -343.284783 494.17466
## 2011.442 67.963023 -206.619610 342.54566 -351.974875 487.90092
## 2011.462 66.970114 -208.400238 342.34047 -354.172497 488.11272
## 2011.481 75.781653 -200.374171 351.93748 -346.562233 498.12554
## 2011.500 59.884169 -217.054899 336.82324 -363.657585 483.42592
## 2011.519 53.984432 -223.735671 331.70453 -370.751813 478.72068
## 2011.538 45.915809 -232.583138 324.41476 -380.011576 471.84319
## 2011.558 41.903997 -237.371623 321.17962 -385.211206 469.01920
## 2011.577 40.122940 -239.927199 320.17308 -388.176787 468.42267
## 2011.596 31.536403 -249.286118 312.35892 -397.944581 461.01739
## 2011.615 29.435157 -252.157629 311.02794 -401.223845 460.09416
## 2011.635 28.415468 -253.945480 310.77642 -403.418337 460.24927
## 2011.654 21.690707 -261.436320 304.81773 -411.314715 454.69613
## 2011.673 21.163282 -262.727756 305.05432 -413.010594 455.33716
## 2011.692 19.358314 -265.294686 304.01131 -415.980881 454.69751
## 2011.712 18.933342 -266.479584 304.34627 -417.568060 455.43474
## 2011.731 18.054301 -268.116534 304.22514 -419.606223 455.71482
## 2011.750 17.772087 -269.154655 304.69883 -421.044496 456.58867
## 2011.769 22.566164 -265.114498 310.24683 -417.403441 462.53577
## 2011.788 17.252242 -271.180369 305.68485 -423.867371 458.37186
## 2011.808 14.972688 -274.209918 304.15529 -427.293943 457.23932
## 2011.827 13.821469 -276.109191 303.75213 -429.589212 457.23215
## 2011.846 14.890692 -275.786098 305.56748 -429.661096 459.44248
## 2011.865 14.254589 -277.166420 305.67560 -431.435384 459.94456
## 2011.885 15.225717 -276.937615 307.38905 -431.599541 462.05098
## 2011.904 11.959911 -280.943863 304.86369 -435.997756 459.91758
## 2011.923 9.414579 -284.227771 303.05693 -439.672641 458.50180
## 2011.942 13.237085 -281.141986 307.61616 -436.976853 463.45102
## 2011.962 9.884584 -285.229370 304.99854 -441.453260 461.22243
## 2011.981 7.660451 -288.186560 303.50746 -444.798508 460.11941
## 2012.000 9.388098 -287.190159 305.96636 -444.189204 462.96540
## 2012.019 7.068660 -290.239044 304.37636 -447.624235 461.76156
## 2012.038 7.101557 -290.933809 305.13692 -448.704201 462.90731
## 2012.058 14.570038 -284.191217 313.33129 -442.345872 471.48595
## 2012.077 14.189330 -285.296055 313.67471 -443.834041 472.21270
## 2012.096 15.008360 -285.199408 315.21613 -444.119800 474.13652
## 2012.115 13.710633 -287.217784 314.63905 -446.519666 473.94093
## 2012.135 19.870139 -281.777206 321.51748 -441.459664 481.19994
## 2012.154 19.151825 -283.212737 321.51639 -443.274868 481.57852
## 2012.173 19.991547 -283.088536 323.07163 -443.529441 483.51254
## 2012.192 25.625985 -278.167934 329.41990 -438.986720 490.23869
## 2012.212 24.794881 -279.711200 329.30096 -440.906983 490.49674
## 2012.231 24.312182 -280.904400 329.52876 -442.476299 491.10066
## 2012.250 29.080870 -276.844562 335.00630 -438.791703 496.95344
## 2012.269 35.024190 -271.608454 341.65683 -433.929971 503.97835
## 2012.288 37.041637 -270.296591 344.37987 -432.991622 507.07490
## 2012.308 39.270488 -268.771709 347.31268 -431.839397 510.38037
## 2012.327 53.920351 -254.824209 362.66491 -418.263706 526.10441
## 2012.346 51.656898 -257.788431 361.10223 -421.598893 524.91269
## 2012.365 64.061793 -246.082721 374.20631 -410.263310 538.38690
## 2012.385 65.815334 -245.026793 376.65746 -409.576675 541.20734
## 2012.404 64.454999 -247.083179 375.99318 -412.001528 540.91153
## 2012.423 75.444938 -236.787739 387.67762 -402.073734 552.96361
## 2012.442 67.963023 -244.962611 380.88866 -410.615436 546.54148
## 2012.462 66.970114 -246.646947 380.58718 -412.665791 546.60602
## 2012.481 75.781653 -238.525314 390.08862 -404.909372 556.47268
## 2012.500 59.884169 -255.111192 374.87953 -421.859664 541.62800
## 2012.519 53.984432 -261.697823 369.66669 -428.809914 536.77878
## 2012.538 45.915809 -270.451848 362.28347 -437.926769 529.75839
## 2012.558 41.903997 -275.147580 358.95557 -442.984547 526.79254
## 2012.577 40.122940 -277.611085 357.85697 -445.809318 526.05520
## 2012.596 31.536403 -286.878607 349.95141 -455.437332 518.51014
## 2012.615 29.435157 -289.659386 348.52970 -458.577834 517.44815
## 2012.635 28.415468 -291.357163 348.18810 -460.634569 517.46551
## 2012.654 21.690707 -298.758578 342.13999 -468.394182 511.77560
## 2012.673 21.163282 -299.961230 342.28779 -469.954278 512.28084
## 2012.692 19.358314 -302.440009 341.15664 -472.789751 511.50638
## 2012.712 18.933342 -303.537383 341.40407 -474.243074 512.10976
## 2012.731 18.054301 -305.087428 341.19603 -476.148326 512.25693
## 2012.750 17.772087 -306.039256 341.58343 -477.454626 512.99880
## 2012.769 22.566164 -301.913410 347.04574 -473.682521 518.81485
## 2012.788 17.252242 -307.894189 342.39867 -480.016313 514.52080
## 2012.808 14.972688 -310.839237 340.78461 -483.313651 513.25903
## 2012.827 13.821469 -312.654592 340.29753 -485.480579 513.12352
## 2012.846 14.890692 -312.248157 342.02954 -485.425003 515.20639
## 2012.865 14.254589 -313.545709 342.05489 -487.072704 515.58188
## 2012.885 15.225717 -313.234696 343.68613 -487.111136 517.56257
## 2012.904 11.959911 -317.159295 341.07912 -491.384478 515.30430
## 2012.923 9.414579 -320.362103 339.19126 -494.935333 513.76449
## 2012.942 13.237085 -317.195764 343.66994 -492.116349 518.59052
## 2012.962 9.884584 -321.203133 340.97230 -496.470383 516.23955
## 2012.981 7.660451 -324.080841 339.40174 -499.694073 515.01498
## 2013.000 9.388098 -323.005483 341.78168 -498.964016 517.74021
## 2013.019 7.068660 -325.975933 340.11325 -502.279092 516.41641
## 2013.038 7.101557 -326.592779 340.79589 -503.239890 517.44300
plot(SJ_ets,xlab= "Time", ylab= "Total Cases")
#IQ#Stlf, Forecasting using stl objects:ETS
IQ_ets<-stlf(iqtot, method="ets", h=156)
summary(IQ_ets)
##
## Forecast method: STL + ETS(A,N,N)
##
## Model Information:
## ETS(A,N,N)
##
## Call:
## ets(y = x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend)
##
## Smoothing parameters:
## alpha = 0.6815
##
## Initial states:
## l = 1.732
##
## sigma: 5.898
##
## AIC AICc BIC
## 5103.597 5103.643 5116.358
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.02301135 5.898046 3.63313 NaN Inf 0.3848585 0.06083529
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2010.115 2.265267 -5.293384 9.823917 -9.294692 13.82522
## 2010.135 3.711101 -5.435922 12.858123 -10.278063 17.70026
## 2010.154 2.534433 -7.963321 13.032186 -13.520496 18.58936
## 2010.173 3.566436 -8.127051 15.259924 -14.317210 21.45008
## 2010.192 4.751518 -8.026294 17.529329 -14.790459 24.29349
## 2010.212 3.989264 -9.787793 17.766320 -17.080926 25.05945
## 2010.231 4.526259 -10.182314 19.234831 -17.968561 27.02108
## 2010.250 6.064286 -9.520223 21.648795 -17.770163 29.89874
## 2010.269 4.195544 -12.218223 20.609311 -20.907146 29.29823
## 2010.288 4.917518 -12.285580 22.120615 -21.392349 31.22738
## 2010.308 8.099780 -9.857987 26.057546 -19.364254 35.56381
## 2010.327 7.715209 -10.966766 26.397184 -20.856406 36.28682
## 2010.346 7.932706 -11.446432 27.311845 -21.705128 37.57054
## 2010.365 15.303072 -4.749006 35.355150 -15.363935 45.97008
## 2010.385 12.754706 -7.948450 33.457861 -18.908038 44.41745
## 2010.404 19.840702 -1.493671 41.175075 -12.787406 52.46881
## 2010.423 14.213371 -7.734073 36.160814 -19.352348 47.77909
## 2010.442 17.567212 -4.976636 40.111061 -16.910628 52.04505
## 2010.462 14.582624 -8.542253 37.707501 -20.783823 49.94907
## 2010.481 9.725908 -13.965752 33.417567 -26.507359 45.95917
## 2010.500 10.978874 -13.266322 35.224071 -26.100954 48.05870
## 2010.519 9.607859 -15.178515 34.394234 -28.299630 47.51535
## 2010.538 12.241378 -13.074608 37.557365 -26.476082 50.95884
## 2010.558 15.321033 -10.513712 41.155777 -24.189799 54.83186
## 2010.577 8.679209 -17.664079 35.022497 -31.609374 48.96779
## 2010.596 3.228069 -23.614130 30.070268 -37.823531 44.27967
## 2010.615 8.482095 -18.849909 35.814099 -33.318599 50.28279
## 2010.635 23.642531 -4.170654 51.455716 -18.894065 66.17913
## 2010.654 14.666734 -13.619448 42.952916 -28.593249 57.92672
## 2010.673 23.628375 -5.123024 52.379773 -20.343095 67.59984
## 2010.692 24.910598 -4.298609 54.119804 -19.761029 69.58222
## 2010.712 25.026278 -4.633671 54.686226 -20.334699 70.38725
## 2010.731 16.859387 -13.244555 46.963330 -29.180620 62.89939
## 2010.750 20.468463 -10.073020 51.009946 -26.240704 67.17763
## 2010.769 17.702826 -13.270017 48.675669 -29.666049 65.07170
## 2010.788 14.054898 -17.343379 45.453175 -33.964623 62.07442
## 2010.808 10.658469 -21.159555 42.476493 -38.002999 59.31994
## 2010.827 9.971630 -22.260675 42.203934 -39.323426 59.26669
## 2010.846 8.052886 -24.588442 40.694213 -41.867717 57.97349
## 2010.865 8.741300 -24.303989 41.786588 -41.797108 59.27971
## 2010.885 7.305609 -26.138761 40.749980 -43.843141 58.45436
## 2010.904 5.757588 -28.081158 39.596334 -45.994308 57.50948
## 2010.923 6.995367 -27.233211 41.223945 -45.352726 59.34346
## 2010.942 4.319817 -30.294203 38.933836 -48.617758 57.25739
## 2010.962 4.394442 -30.600775 39.389658 -49.126124 57.91501
## 2010.981 5.533757 -29.838549 40.906062 -48.563516 59.63103
## 2011.000 4.579384 -31.166033 40.324801 -50.088513 59.24728
## 2011.019 5.064039 -31.050634 41.178713 -50.168587 60.29667
## 2011.038 5.295188 -31.185004 41.775381 -50.496452 61.08683
## 2011.058 3.049981 -33.792105 39.892066 -53.295127 59.39509
## 2011.077 2.927968 -34.272490 40.128426 -53.965223 59.82116
## 2011.096 3.270107 -34.285305 40.825518 -54.165938 60.70615
## 2011.115 2.265267 -35.641774 40.172307 -55.708549 60.23908
## 2011.135 3.711101 -34.544338 41.966539 -54.795543 62.21774
## 2011.154 2.534433 -36.066258 41.135124 -56.500230 61.56910
## 2011.173 3.566436 -35.376447 42.509320 -55.991565 63.12444
## 2011.192 4.751518 -34.530577 44.033613 -55.325263 64.82830
## 2011.212 3.989264 -35.629139 43.607666 -56.601854 64.58038
## 2011.231 4.526259 -35.425620 44.478138 -56.574867 65.62739
## 2011.250 6.064286 -34.218308 46.346881 -55.542626 67.67120
## 2011.269 4.195544 -36.415074 44.806162 -57.913036 66.30412
## 2011.288 4.917518 -36.018494 45.853530 -57.688710 67.52375
## 2011.308 8.099780 -33.159061 49.358620 -55.000171 71.19973
## 2011.327 7.715209 -33.863953 49.294372 -55.874632 71.30505
## 2011.346 7.932706 -33.964329 49.829741 -56.143279 72.00869
## 2011.365 15.303072 -26.909442 57.515586 -49.255398 79.86154
## 2011.385 12.754706 -29.770947 55.280359 -52.282668 77.79208
## 2011.404 19.840702 -22.995801 62.677205 -45.672076 85.35348
## 2011.423 14.213371 -28.931743 57.358484 -51.771386 80.19813
## 2011.442 17.567212 -25.884319 61.018744 -48.886171 84.02060
## 2011.462 14.582624 -29.173181 58.338429 -52.336105 81.50135
## 2011.481 9.725908 -34.332068 53.783884 -57.654952 77.10677
## 2011.500 10.978874 -33.379215 55.336964 -56.860969 78.81872
## 2011.519 9.607859 -35.048326 54.264045 -58.687883 77.90360
## 2011.538 12.241378 -32.710926 57.193683 -56.507239 80.99000
## 2011.558 15.321033 -29.925454 60.567519 -53.877497 84.51956
## 2011.577 8.679209 -36.859559 54.217976 -60.966326 78.32474
## 2011.596 3.228069 -42.601116 49.057254 -66.861620 73.31776
## 2011.615 8.482095 -37.635678 54.599868 -62.048953 79.01314
## 2011.635 23.642531 -22.762036 70.047098 -47.327130 94.61219
## 2011.654 14.666734 -32.022865 61.356332 -56.738846 86.07231
## 2011.673 23.628375 -23.344526 70.601276 -48.210478 95.46723
## 2011.692 24.910598 -22.343908 72.165103 -47.358932 97.18013
## 2011.712 25.026278 -22.508163 72.560719 -47.671376 97.72393
## 2011.731 16.859387 -30.953350 64.672125 -56.263885 89.98266
## 2011.750 20.468463 -27.620961 68.557887 -53.077964 94.01489
## 2011.769 17.702826 -30.661701 66.067354 -56.264336 91.66999
## 2011.788 14.054898 -34.583177 62.692973 -60.330619 88.44041
## 2011.808 10.658469 -38.251624 59.568562 -64.143063 85.46000
## 2011.827 9.971630 -39.208976 59.152236 -65.243616 85.18688
## 2011.846 8.052886 -41.396753 57.502525 -67.573811 83.67958
## 2011.865 8.741300 -40.975917 58.458516 -67.294621 84.77722
## 2011.885 7.305609 -42.677752 57.288971 -69.137346 83.74856
## 2011.904 5.757588 -44.490509 56.005685 -71.090245 82.60542
## 2011.923 6.995367 -43.516078 57.506812 -70.255222 84.24596
## 2011.942 4.319817 -46.453611 55.093244 -73.331439 81.97107
## 2011.962 4.394442 -46.639623 55.428506 -73.655424 82.44431
## 2011.981 5.533757 -45.759620 56.827134 -72.912694 83.98021
## 2012.000 4.579384 -46.972002 56.130769 -74.261657 83.42042
## 2012.019 5.064039 -46.744070 56.872148 -74.169626 84.29770
## 2012.038 5.295188 -46.768379 57.358755 -74.329166 84.91954
## 2012.058 3.049981 -49.267796 55.367758 -76.963155 83.06312
## 2012.077 2.927968 -49.642790 55.498726 -77.472069 83.32800
## 2012.096 3.270107 -49.552421 56.092634 -77.514978 84.05519
## 2012.115 2.265267 -50.807836 55.338370 -78.903040 83.43357
## 2012.135 3.711101 -49.611400 57.033601 -77.838627 85.26083
## 2012.154 2.534433 -51.036304 56.105170 -79.394940 84.46381
## 2012.173 3.566436 -50.251393 57.384265 -78.740830 85.87370
## 2012.192 4.751518 -49.312274 58.815309 -77.931916 87.43495
## 2012.212 3.989264 -50.319376 58.297903 -79.068633 87.04716
## 2012.231 4.526259 -50.026130 59.078648 -78.904421 87.95694
## 2012.250 6.064286 -48.730768 60.859341 -77.737517 89.86609
## 2012.269 4.195544 -50.841106 59.232194 -79.975748 88.36684
## 2012.288 4.917518 -50.359671 60.194707 -79.621647 89.45668
## 2012.308 8.099780 -47.416906 63.616466 -76.805665 93.00522
## 2012.327 7.715209 -48.039945 63.470364 -77.554941 92.98536
## 2012.346 7.932706 -48.059901 63.925314 -77.700597 93.56601
## 2012.365 15.303072 -40.925985 71.532129 -70.691850 101.29799
## 2012.385 12.754706 -43.709811 69.219223 -73.600321 99.10973
## 2012.404 19.840702 -36.858297 76.539701 -66.872935 106.55434
## 2012.423 14.213371 -42.719145 71.145886 -72.857398 101.28414
## 2012.442 17.567212 -39.597866 74.732291 -69.859230 104.99366
## 2012.462 14.582624 -42.814074 71.979322 -73.198051 102.36330
## 2012.481 9.725908 -47.901480 67.353295 -78.407576 97.85939
## 2012.500 10.978874 -46.878283 68.836031 -77.506011 99.46376
## 2012.519 9.607859 -48.478158 67.693877 -79.227039 98.44276
## 2012.538 12.241378 -46.072601 70.555358 -76.942158 101.42491
## 2012.558 15.321033 -43.220022 73.862087 -74.209784 104.85185
## 2012.577 8.679209 -50.088043 67.446461 -81.197547 98.55596
## 2012.596 3.228069 -55.764513 62.220651 -86.993299 93.44944
## 2012.615 8.482095 -50.734959 67.699149 -82.082574 99.04676
## 2012.635 23.642531 -35.798148 83.083210 -67.264143 114.54920
## 2012.654 14.666734 -44.996732 74.330200 -76.580663 105.91413
## 2012.673 23.628375 -36.257049 83.513798 -67.958477 115.21523
## 2012.692 24.910598 -35.195964 85.017159 -67.014456 116.83565
## 2012.712 25.026278 -35.300612 85.353167 -67.235738 117.28829
## 2012.731 16.859387 -43.687028 77.405803 -75.738364 109.45714
## 2012.750 20.468463 -40.296685 81.233611 -72.463811 113.40074
## 2012.769 17.702826 -43.280270 78.685923 -75.562771 110.96842
## 2012.788 14.054898 -47.145371 75.255167 -79.542836 107.65263
## 2012.808 10.658469 -50.758204 72.075142 -83.270226 104.58716
## 2012.827 9.971630 -51.660687 71.603947 -84.286865 104.23012
## 2012.846 8.052886 -53.794324 69.900095 -86.534259 102.64003
## 2012.865 8.741300 -53.320058 70.802658 -86.173357 103.65596
## 2012.885 7.305609 -54.969161 69.580379 -87.935433 102.54665
## 2012.904 5.757588 -56.729865 68.245041 -89.808725 101.32390
## 2012.923 6.995367 -55.704048 69.694781 -88.895113 102.88585
## 2012.942 4.319817 -58.590846 67.230479 -91.893739 100.53337
## 2012.962 4.394442 -58.726761 67.515644 -92.141107 100.92999
## 2012.981 5.533757 -57.797287 68.864800 -91.322716 102.39023
## 2013.000 4.579384 -58.960807 68.119575 -92.596952 101.75572
## 2013.019 5.064039 -58.684613 68.812692 -92.431111 102.55919
## 2013.038 5.295188 -58.661246 69.251623 -92.517738 103.10811
## 2013.058 3.049981 -61.113563 67.213524 -95.079691 101.17965
## 2013.077 2.927968 -61.442018 67.297954 -95.517431 101.37337
## 2013.096 3.270107 -61.305663 67.845876 -95.490010 102.03022
plot(IQ_ets,xlab= "Time", ylab= "Total Cases")
#Model 3: Submission
par(mfrow=c(1,2))
plot(SJ_ets,xlab= "Time", ylab= "Total Cases")
plot(IQ_ets,xlab= "Time", ylab= "Total Cases")
x3$total_cases[1:260]<-round(SJ_ets$mean)
x3$total_cases[261:416]<-round(IQ_ets$mean)
write.csv(x3, file="~/Desktop/sub..3.csv", row.names = FALSE)
#SCORE= 26.6683