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