My question is in continuation to the these questions: Question 1 Question 2 Question 3 Question 4

I am also attempting to automate the forecasting of financial parameters of multiple products in the Automobile sector. For this, I am planning to use the ets, forecast functions.

About my data:

  • Timeseries having 50 data points (monthly data for 4 years)
  • Some products begin a little later than the others and therefore have lesser observation points.
  • Usually, the data of all the product is seasonal.

My question is more concerned with the pre and post forecast steps i.e.,

  • Data prepartion and
  • Model fit/accuracy


  1. In the automation code, I would want to create a chunk which does an automated data preparation, as needed by the product. Now, is there something similar to the forecast package which automatically does the data transformation too? I have tried the lambda and boxcox in the forecast function, but am not getting very good results.
    If not, could anyone suggest, what must the bare minimum tests be? For example, I see that nsdiff and ndiff will help us in identifying if differencing is needed and the level too.

    1. Is it advisable to completely remove all those products from the analysis which have lesser datapoints or have a bad quality ( like, maximum observations= 0). If I include them in the analysis, would it effect the analysis of other products?

Really looking forward to a guidance.


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