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I am doing time series forecasting for estimating monthly sales of certain consumer goods SKU.

I have last 3 years of actual sales data, wherein first 2.5 years of data is monthly sales data, but last 6 months data daily sales data is available.

I can certainly add the daily data to get monthly data, but considering that total data points are then only 36 (for 3 months) and it may be slightly challenging to get various models like ARIMA, Exponential models.

Is there a way to use the daily last 6 months of data to improve the accuracy of the model?

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I would do two separate analyses:

  • By day for six months
  • By month for the last 36 months (with the most recent 6 aggregated)

The 36 month analysis will be important for catching annual trends you wouldn't be able to see with the daily data, but you'll have a lot more fine-grain with the daily analysis to catch quarterly, monthly, and weekly cycles.

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  • $\begingroup$ If this helped, don't forget to mark it answered :) $\endgroup$ Commented Oct 5, 2017 at 20:31
  • $\begingroup$ How to do mark it answered CalZ? $\endgroup$ Commented Oct 7, 2017 at 1:37
  • $\begingroup$ I believe there should be a check mark next to the vote totals to the left of your post. $\endgroup$ Commented Oct 7, 2017 at 13:35
  • $\begingroup$ Done CalZ.. Thanks.. Am new to Stackexchange.. Shall ensure I do that for any query that gets clarified here.. $\endgroup$ Commented Oct 9, 2017 at 8:09

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