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n_input = 25 #how many samples/rows/timesteps to look in the past in order to forecast the next sample | |
n_features= X_train.shape[1] # how many predictors/Xs/features we have to predict y | |
b_size = 32 # Number of timeseries samples in each batch | |
generator = TimeseriesGenerator(scaled_X_train, scaled_y_train, length=n_input, batch_size=b_size) | |
print(generator[0][0].shape) | |
>>> (32, 25, 3) |
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