This question refers to the data set that you are using for your project. For your data set determine the best forecasting method (average, naïve, seasonal naïve, exponential smoothing, Holt-Winters’ method, ETS, ARIMA). ). Use 80% of the data for training and 20% of the data for testing. Using the best model, forecast your data set for 6 periods into the future.

Load Data and Library Packages
Define as time-series and name varaible
indigo_data = ts(indigo_data, start=2002, frequency=4)
data = indigo_data[,3]
Set training data, test data, out of sample
train <- window(data,start=c(2002, 1),end=c(2014, 2))
test <- window(data, start=c(2014,2),end=c(2017, 3))
both <- window(data,start=c(2002, 1))
Forecast using Average, Naïve, and Seasonal Naïve Method
Indigofit1 <- meanf(train, h=h)
Indigofit2 <- naive(train, h=h)
Indigofit3 <- snaive(train, h=h)
Plot forecasts for Average, Naïve, and Seasonal Naïve Method
plot(Indigofit1, PI=FALSE,
     main="Forecasts for quarterly Indigo sales")
       legend=c("Mean method","Naive method","Seasonal naive method"),bty="n")

Forecast using Simple Exonential moving averages
Indigofit4 <- ses(train, h = h)