I learnt how to use DL method to deal with structure data in the course, and want to use the method to deal with this (challenge)[https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting]. I just found that the kernels on LB would like to leverage all kinds statistics across different time frames to predict visitors, and am wondering is that appropriate to combine fast.ai structure data processing pipeline and LSTM in order to preventing tricky feature engineering ? Thanks.
Thank you. It looks like we have similiar questions. These days I tried out some kernels on leaderboard, and guess that even thought LSTM is able to memorise previous status of a system, it is not able to figure out appropriate feature representation for a centain problem if there are not enough data. Thus, I am still unable to find a ’ silver bullet ’ for time series structure data problems.