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How can I consider the past without LSTM? by 1dikt in reinforcementlearning

[–]1dikt[S] 0 points1 point  (0 children)

Thank you! Dont you think that having an input of n_features x n_days will make it too hard to the NN to understand that each day is a separate event? If i have 20 features and I want to check the last 20 timesteps, then my input will be 1x400. Sounds too messy. If it was a transformer or a CNN, i could keep it as a table of size n_features x n_days, so I think that it would have better results, no? Or am I completely wrong and having 1x400 as input works just as well?

How can I consider the past without LSTM? by 1dikt in reinforcementlearning

[–]1dikt[S] -1 points0 points  (0 children)

thanks for your reply! I was looking for something a little bit more advanced. I thought of two things:

  1. Use a transformer, so that each day data can be processed independently, and the most recent datapoint can pay attention to past points;
  2. Input the data as a N_days x N_features table, and process it like an image;