First, I read the Q&As here, but it's not entirely clear for me what to do given my situation.
I have more than 1700 different time series, each expressed as weekly data. My objective is to produce "good enough" forecasts (in terms of accuracy) for the next season/quarter. The length of my longest time series is 117 weekly data points, which starts halfway 2016.
The models I am currently considering are the simplest models: naive, seasonal naive, mean, random walk + drift. I would also like to consider some exponential smoothing models and potentially an ARIMA model using
auto.arima() from the
forecast package in R. I wonder:
- Can I just keep the data weekly and produce forecasts 13 steps (weeks) ahead?
- I am interested in forecasting one season ahead, so would it be best to build my models in quarterly data or will my time series become much too 'short'? (in my dataset I have an indicator for which season / quarter the week belongs to). This would mean I have a maximum of 9 quarters and for most time series much less (this could be even only 2).
- Follow up on 2: as a maximum of 9 data points in a time series seems (too) few, would it be good to aggregate my weekly data to monthly data and produce 3 months ahead forecasts based on this?