I am sorry if this is too general of a question, but I am stuck somewhere between perfect and adequate in my model. So, I wanted to ask here. If it is not a suitable question, your negative feedbacks are all welcome, sorry. But theoretically subject is inside a gray area and all comments are valuable I think. It is not a widespread approach to use ML for forecasting, as far as I know.

Our sales team publishes a forecasting report at the beginning of each year. But it is mostly far from reality, not very scientific. It includes subjective assesments also.

We decided to construct a ML model to make a forecasting. Not a classical forecasting because there are many input variables that can effect the output. So, it is an experimental project actually.

So far, I have developed an adequate model, which is already far more better than sales one. But still I think it is not a good one, only adequate.

Data includes Customer-FromCountry-ToCountry based sales. So there are many combinations. By looking at similar applications at MachineLearningMastery.com, I have developed my model like that:

customer-from-to-M9-M8.....M1 - month - quarter - movingavg3 - mov6 - mov9 - dptcountryholidaycount - arvcountryholidaycount

m9 to m1 are previous 9 month sales of predicted month M(output variable) M's vary from 0 to 800 mostly. Holidays are from 0 to 6 for example. I Scaled all of these numerical variables between 0-1

Customer-From-To-Month-Quarter variables are categorical and I used OneHotEncoding for these.

I shuffled all the data, so there is not a time ordered dependency inside my dataset.

I have divided the data into 3 segments. 1: above 100 average for last 3 months; 2:10-100 average 3: below 0 average for 3 months. I run different models for each 3 segments.

There are many 0 values, so I am using a LGBClassifier first to determine if the sales is 0 or not. Then if it is not 0, I am using a LGBRegressor to determine the sales value for that input row. (SGDRegressor for segment 1, it performs better)

I tried to add MonthSeasonality index and QuarterSeasinality index for each combination into dataset as variables. But it didn't perform well.

What can I try to get better score, I am not asking about algorithms etc. My main purpose here is to get insights about construction of dataset, preprocessing. Your precious ideas about preprocessing tricks before applying a ML algorithm.



1 Answer 1


I'm afraid your data is probably too complex and specific for somebody else to understand exactly what's going on.

The only idea I can suggest is to try to analyze manually the errors that your model makes:

  • Are there any patterns, like a kind of errors which happens quite often? For instance a country which tends to be overestimated, a month which tends to be under-estimated, this kind of thing.
  • If yes try to investigate why: for instance is there some additional feature that could help the model make better predictions for these cases?

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