Exponential smoothing forecasting for multiple time series

Question

I would like to forecast 100x time series in r using exponential smoothing (HW or ARIMA) because my data is very seasonal. My data is currently setup like:

Month / Employee1 / Employee2 / Employee3 / ...
2015-01-31 / 1,200,000 / 1,900,000 / 800,000 / ...
2015-02-28 / 1,000,000 / 1,700,000 / 200,000 / ...
... Through 2018-06-30

I would like to forecast using exponential smoothing for each employee for 6 periods where frequency = 12. I can do this easily individually using forecast, but I would like to run all employees at once into a single table output. The confidence level can equal to 0 level=c(0,0) since I want a single output.

Any help is much appreciated!

Answer 1

I figured it out and hopefully it will help in the future. My data set is called "Multiforecast_test"

First I made it into a ts:

ts_test <- ts(Multiforecast_test, start= c(2015,01), frequency=12)

Then I used lapply to run the auto.arima function:

arimaforecast <- lapply(ts_test, function(x) forecast(auto.arima(x), h=6,)$mean)

The output for my test data results in:

arimaforecast

$Volume        Jul        Aug        Sep        Oct        Nov        Dec
2018 1005256299 1107626858  929720018  889901375  814714019  839815505

$Employee1     Jul       Aug       Sep       Oct       Nov       Dec
2018 683043831 800911854 686582210 665243135 613016109 626239041

$Employee2     Jul       Aug       Sep       Oct       Nov       Dec
2018 198639231 206957874 138334667 148160835 118637508 111321392

$Employee3     Jul       Aug       Sep       Oct       Nov       Dec
2018 116508747 129413942 111011512  90294567  87439508  92747072

...

Hopefully this helps someone in the future.