DIS5

library(hts)

## Loading required package: forecast

library('dplyr')

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

train<- read.csv("Downloads/dengue_features_train.csv")
test <- read.csv("Downloads/dengue_features_test.csv")
labels_train <- read.csv("Downloads/dengue_labels_train.csv")


#There are two cities, iq and sj. So we didvid the data 
iqtrain = train %>% filter(city == 'iq')
sjtrain = train %>% filter(city == 'sj')

#Using hts paackages
sjhts <- hts(sjtrain)

## Since argument characters are not specified, the default labelling system is used.

plot(sjhts)

iqhts <- hts(iqtrain)

## Since argument characters are not specified, the default labelling system is used.

plot(iqhts)