You have to prepare your data as a numpy array with the following shape:
( Number of observations , Input length , Number of variables )
Assuming you are working with Keras, the input of the LSTM() layer is as above, but you don't need to report the number of observations: input_shape = (Input length , Number of variables). Input length is an hyperparameter of your choice.
I pushed this Notebook on GitHub that contains a function to preprocess your dataset for RNNs:
def univariate_processing(variable, window):
''' RNN preprocessing for single variables.
Can be iterated for multidimensional datasets. '''
import numpy as np
# create empty 2D matrix from variable
V = np.empty((len(variable)-window+1, window))
# take each row/time window
for i in range(V.shape[0]):
V[i,:] = variable[i : i+window]
V = V.astype(np.float32) # set common data type
return V
def RNN_regprep(df, y, len_input, len_pred): #, test_size):
'''
RNN preprocessing for multivariate regression. Builds multidimensional
dataset by iterating univariate preprocessing steps.
Requires univariate_processing() function.
Args: df, y: X and y data in numpy.array() format
len_input, len_pred: length of input and prediction sequences
Returns: X, Y matrices
'''
import numpy as np
# create 3D matrix for multivariate input
X = np.empty((df.shape[0]-len_input+1, len_input, df.shape[1]))
# Iterate univariate preprocessing on all variables - store them in XM
for i in range(df.shape[1]):
X[ : , : , i ] = univariate_processing(df[:,i], len_input)
# create 2D matrix of y sequences
y = y.reshape((-1,)) # reshape to 1D if needed
Y = univariate_processing(y, len_pred)
## Trim dataframes as explained
X = X[ :-(len_pred + 1) , : , : ]
Y = Y[len_input:-1 , :]
# Set common datatype
X = X.astype(np.float32)
Y = Y.astype(np.float32)
return X, Y
Let me know if that's what you were looking for.
