I conduct time series by day for SALES. I have dataset with data by day. (format 01.11.2015-29.11.2015). Here the example:

```
dput
DAY        STORE    ART     SALES
01.11.2015  1534    343533  62.5000
01.11.2015  25039   20490   686.4480
01.11.2015  1612    295206  185.0000
01.11.2015  1053    16406274    32.5000
01.11.2015  1612    49495   143.1196
01.11.2015  961 15309949    50.9000

```

How to do forecast for all shops and ART ot once, how to split my analysis on two factor?

#

```
library('ggplot2')
library('forecast')
library('tseries')

mydat=read.csv("C:/Users/synthex/Downloads/sales.csv", sep=";",dec=",")
View(mydat)
str(mydat)

count_ts = ts(mydat[, c('SALES')])
View(count_ts)

mydat$clean_cnt = tsclean(count_ts)

mydat$cnt_ma = ma(mydat$clean_cnt, order=7) # using the clean count with no outliers
mydat$cnt_ma30 = ma(mydat$clean_cnt, order=30)

count_ma = ts(na.omit(mydat$cnt_ma), frequency=30) 
decomp = stl(count_ma, s.window="periodic")
deseasonal_cnt <- seasadj(decomp)
plot(decomp) 

adf.test(count_ma, alternative = "stationary") 

auto.arima(deseasonal_cnt, seasonal=FALSE)

fit<-auto.arima(deseasonal_cnt, seasonal=FALSE)

tsdisplay(residuals(fit), lag.max=45, main='(1,1,0) Model Residuals')

fit2 = arima(deseasonal_cnt, order=c(1,1,7))

fcast <- forecast(fit2, h=1)

```