I need to forecast sales of hundreds of products in a retail store. I have weekly sales data of each for 2.5 years (133 weeks). The value I want to predict is actually aggregated demand for each SKU for the next 3 months (to make inventory orders). The individual sales in each week is not important.
How do I go about this problem? Do I again aggregate the data to monthly and predict for 3 periods in future or do I leave it as weekly data? The issue is that aggregating the time series to monthly will reduce the data points to just 33.
The sales of each SKU is in itself very noisy so I used hierarchical forecasting with weekly data but there seems to be too much noise in the top category itself as seen in picture. weekly aggregated sales of all SKUs [top of the hierarchy]
Will I be able to do a good forecast for the time series in picture? I've tried arima with fourier terms (k=14) and they didnt give that good results neither did tbats. I'm thinking of doing monthly aggregation and using ets.