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## Libraries
library(fastDummies)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
library(generics)
## 
## Attaching package: 'generics'
## The following object is masked from 'package:caret':
## 
##     train
## The following object is masked from 'package:lubridate':
## 
##     as.difftime
## The following objects are masked from 'package:base':
## 
##     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
##     setequal, union
library(tsibble)
## 
## Attaching package: 'tsibble'
## The following object is masked from 'package:lubridate':
## 
##     interval
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, union
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:generics':
## 
##     explain
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(fpp3)
## ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ──
## ✓ tibble      3.1.6     ✓ feasts      0.2.2
## ✓ tidyr       1.2.0     ✓ fable       0.3.1
## ✓ tsibbledata 0.4.0
## ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
## x lubridate::date()    masks base::date()
## x dplyr::filter()      masks stats::filter()
## x tsibble::intersect() masks generics::intersect(), base::intersect()
## x tsibble::interval()  masks lubridate::interval()
## x dplyr::lag()         masks stats::lag()
## x fabletools::MAE()    masks caret::MAE()
## x fabletools::RMSE()   masks caret::RMSE()
## x tsibble::setdiff()   masks generics::setdiff(), base::setdiff()
## x tsibble::union()     masks generics::union(), base::union()
library(modeest)
library(forecast)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
## Registered S3 method overwritten by 'forecast':
##   method          from  
##   predict.default statip
## 
## Attaching package: 'forecast'
## The following object is masked from 'package:modeest':
## 
##     naive
## The following objects are masked from 'package:generics':
## 
##     accuracy, forecast
library(latex2exp)
library(seasonal)
## 
## Attaching package: 'seasonal'
## The following object is masked from 'package:tibble':
## 
##     view
## Data Set
df <- read.csv("//Users//kevinclifford//Downloads//Alcohol_Sales.csv", header=TRUE)

df$Sales <- df$S4248SM144NCEN
df$S4248SM144NCEN <- NULL

ts <- ts(df$Sales, frequency = 12, start=c(1992))

plot(ts)

## ETS Models
fit1 <- ets(ts)
fit1
## ETS(M,Ad,M) 
## 
## Call:
##  ets(y = ts) 
## 
##   Smoothing parameters:
##     alpha = 0.0805 
##     beta  = 0.0232 
##     gamma = 1e-04 
##     phi   = 0.9592 
## 
##   Initial states:
##     l = 4199.083 
##     b = 3.4466 
##     s = 1.1642 1.0362 1.0338 0.9829 1.0534 1.0081
##            1.106 1.0665 0.9754 0.9758 0.8275 0.7702
## 
##   sigma:  0.0455
## 
##      AIC     AICc      BIC 
## 5672.314 5674.549 5740.422
plot(fit1)

accuracy(fit1)
##                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
## Training set 53.45892 364.3906 285.5164 0.5004852 3.657119 0.6753521 -0.2918586
fe <- forecast(fit1, 12)
acc <- accuracy(fe, df$Sales[1:12])
acc
##                       ME      RMSE       MAE          MPE       MAPE      MASE
## Training set    53.45892  364.3906  285.5164    0.5004852   3.657119  0.312568
## Test set     -9484.70207 9600.8980 9484.7021 -228.2510458 228.251046 10.383342
##                    ACF1
## Training set -0.2918586
## Test set             NA
plot(fe, main="MMN")

arima1 <- auto.arima(ts)
arima1
## Series: ts 
## ARIMA(3,1,1)(0,1,2)[12] 
## 
## Coefficients:
##           ar1     ar2     ar3      ma1     sma1     sma2
##       -0.1428  0.1580  0.5125  -0.9483  -0.2601  -0.2642
## s.e.   0.0637  0.0651  0.0609   0.0328   0.0581   0.0543
## 
## sigma^2 = 102379:  log likelihood = -2242.28
## AIC=4498.56   AICc=4498.93   BIC=4524.77
plot(arima1)

accuracy(arima1)
##                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
## Training set 34.22564 310.4741 232.8522 0.3437269 2.939946 0.5507817 0.02751723
fe2 <- forecast(arima1, 12)
acc2 <- accuracy(fe2, df$Sales[1:12])
acc2
##                       ME      RMSE       MAE          MPE       MAPE       MASE
## Training set    34.22564  310.4741  232.8522    0.3437269   2.939946  0.2549141
## Test set     -9644.03404 9745.1509 9644.0340 -232.2911945 232.291195 10.5577699
##                    ACF1
## Training set 0.02751723
## Test set             NA
plot(fe2, main="Auto-ARIMA")

train <- ts(df$Sales[1:319], frequency = 12, start = c(1992))
test <- ts(df$Sales[320:325], frequency = 12, start=c(2018, 8))
ets_train  <- ets(train)
forecast_ets <- forecast(ets_train, h=6)
autoplot(ts) +
  autolayer(forecast_ets, series = "ETS Model") 

arima_train <- auto.arima(train)
forecast_arima <- forecast(arima_train, h=6)
autoplot(ts) +
  autolayer(forecast_arima, series = "ARIMA Model") 

ets_acc <- accuracy(forecast_ets, test)
ets_acc
##                     ME     RMSE      MAE       MPE     MAPE      MASE
## Training set  17.67402 357.2065 282.4461 0.1167282 3.674768 0.6856084
## Test set     417.98531 555.1506 531.6242 3.1123649 4.029104 1.2904623
##                    ACF1 Theil's U
## Training set -0.3562882        NA
## Test set     -0.4140589  0.230625
arima_acc <- accuracy(forecast_arima, test)
arima_acc
##                     ME     RMSE      MAE       MPE     MAPE      MASE
## Training set  32.05491 307.1495 230.0796 0.3651227 2.940372 0.5584942
## Test set     501.53692 565.8644 513.9100 3.6570141 3.756829 1.2474628
##                     ACF1 Theil's U
## Training set  0.03072723        NA
## Test set     -0.02538462 0.2383725