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
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
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
forecastfunction, but am not getting very good results.
If not, could anyone suggest, what must the bare minimum tests be? For example, I see that
ndiffwill help us in identifying if differencing is needed and the level too.
- 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.