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My goal is to create a time series model with

  1. Multiple Entities - I have multiple products with pre orders and they all have the a similar bell shaped curve peeking at the release date of the product but different orders of magnitude in unit salles OR I can use their cumulative slaes what is an "S" shaped curve. But I only have about 100 products 1 year of daily data to do the training on.
  2. Multivariate - I have a wide variety of data on these indie movies for each day: A.) number of times people added them to the Wishlist, B.) page views, C.) time spent on the page AVG, -> Y.) Target value is the number of products people payed for (it is the same pre order before release and normal purchase after release date)
  3. Multi-step - predicting 60 days ahead would be the goal
  4. Every day refreshing the predictions for every product - Does this requires me to retrain the modell on the whole dataset?

Already Read

  • I have found algorithms that can do prediction on 1 variate maybe even Multivariate. Multi-step is already problematic and I don't know how to add the Multiple Entities part at all. So I cant fine a project or guide that would contain all these 3 parts that I nd
  • I have tried LSTM (13 different models with different datasets) but on longer "Multi-step" it is not working so more than 1 or 2 days. I also cant make the LSTM to accept Multiple Entities so I just chained each products data after each other historically, I do understand that it is not an optimal practice for sure.
  • Python package non popular so I cant find projects to it - https://stats.stackexchange.com/a/412355/256200
  • I always see this R guide but I don't use R. I need help with Python - https://otexts.com/fpp2/hierarchical.html
  • Not multiple variable and not Multi-step - https://stats.stackexchange.com/questions/356008/multiple-time-series-prediction-python
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    $\begingroup$ Have you tried XGBoost? It seems difficult at first view to predict 60 days with only 365 days of training, but it could be possible with some data pre processing. I had to deal with similar problems but with stock markets, and I had to find the right time range sampling (ex: grouping the mean values for one week) to make better predictions. $\endgroup$ Commented Jul 2, 2021 at 13:57
  • $\begingroup$ I have used XGBOOST before for Regression and Classification. I am not sure how to push the target value historically back to predict the future. I am not sure about his whole turning time series problem to regression problem. I would love to see a guide on it. $\endgroup$
    – sogu
    Commented Jul 2, 2021 at 14:21
  • $\begingroup$ I've found this code but I don't know if it could be helpful or not: programmersought.com/article/96993038054 $\endgroup$ Commented Jul 3, 2021 at 7:43

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