Python Keras LSTM Features order relevance
The order of the features does not make much of a difference as you will have a separate output node for each feature.
What matters is the order of the events (the rows). This is the sequence you will be modelling and predicting with the model. To determine which features to use you must know what you are modelling. Putting all of them in will give you predictions for all the columns but will also probably make the model harder to train and optimize.
So you should think about which of the features you actually need to include in the model. For example, can you infer feature A from feature B? If so, feature A does not need to be included as its easy to calculate.
What about features that do not add much information such as columns that do not change very often. Are they necessary?
I want to know if the order of features on array interferes on training model and validation scores.
For exemple if I had this list of features to train my model:
["close", "close_returns", "log_returns", "open", "open_returns", "open_log_returns"]
If I invert de order like:
["close", "open","log_returns", "close_returns", "open_log_returns", "open_returns"]
This make any difference on predicted model? Or the sort order dosent interfere?
If the order interfere. How I can know what the beast order?
And how I can know if determinate feature is relevant or not for my lstm model on Keras?