Finding "look_back" & "look_ahead" hyper-parameters for Seq2Seq models

For Seq2Seq deep learning architectures, viz., LSTM/GRU and multivariate, multistep time series forecasting, its important to convert the data to a 3D dimension: (batch\_size, look\_back, number\_features). Here \_look\_back\_ decides the number of past data points/samples to consider using \_number\_features\_ from your training dataset. Similarly, \_look\_ahead\_ needs to be defined which defines the number of steps in future, you want your model to forecast for.

I have a written a function to help achieve this:

def split_series_multivariate(data, n_past, n_future):
'''
Create …

neuralnetworks seq2seq