What time-series frequency should I use? Weekly, monthly, quarterly 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? 

 A: *

*Yes, you can do that. If you are considering auto.arima(), then simply use forecast(..., h=13) and look at ?forecast.Arima (note the capitalization).


*You can either forecast your weekly data and aggregate the forecasts, or model and forecast on quarterly granularity. It's hard to say offhand which one will be more accurate. (If you want to be fancy, you can do both and reconcile the forecasts using the MAPA algorithm, Kourentzes et al., 2014 - take a look at the MAPA package for R).
If you have only two quarters' worth of data, then yes, that is not much. But then again, that is just 26 weeks, which I would not consider much more reliable to forecast 13 weeks out.


*As above, we can't tell you which approach would be most accurate. Try both aggregating-then-forecasting and forecasting-then-aggregating (and potentially MAPA) and check for yourself which one is better on your particular data, using a holdout sample. I personally believe that MAPA is worthwhile, but I won't declare that it will always improve accuracy.
