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I'm hesitating between these two techniques for business data (activity logs, purchases) for classification:

  • I take all the data and consider it as a multidimensional time serie and use a deep learning model for time series

  • I calculate mean, sum, number of records, variance for each feature instead of using the whole time serie and then I use xgboost

Anyone knows the pros and cons ?

They are about 70 different features and a million of lines in the dataset

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In my personal opinion, I wouldn´t use DL models for time series regression no matter how much data is available. They are hard to train in this type of task and you´ll be requiring a huge amount of data and time to get great results. Consider a DL model would require more features than just the plain time serie.

XGBoost is more friendly and requires less time and data for training. A disavantage could be the amount of features, as you give more to the model, it takes longer to train, I think 70 features would not be a big deal for it.

Finally, I suggest doing a feature selection just to ensure data quality and speed up the training of whatever model you´ll be working on.

Hope it helps!

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  • $\begingroup$ It helps thanks. I've already used xgboost but I was curious about the DL method. I keep in mind it would be usefull with other data $\endgroup$
    – Jiayme C
    Commented Aug 13, 2020 at 10:26

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