I would like to start a discussion. I am dealing with a consumption forecasting project and analyzing a database composed of more than 60k id counters.

The goal is to create a system able to model the series efficiency and avoiding high time-consuming. I kept the following two approaches:

  1. Sarima model applied to each time series, but it takes too much time (1.2 months) because of the Grid Search Approach useful to fit all the parameters in each id.
  2. Fastai library, able to create a Tabular Data Bunch and a unique model for all the series, which has lower performance than the classical approach but it is advantageous by the time-consuming side (only 4 hours training).

In the first approach, I only used the historical consumption values, while, for the second approach I considered more features such as temperature, humidity, building id, apartment id, city, and so on. Actually I am trying to create a model using Keras and Tensorflow, in order to control and create the model, but I don't see improvements.

It seems I am not able to reach a good settlement between performance accuracy and fast model creation. Do you have any suggestions? Any similar experience on this?


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