multiVarMultiStepLSTM.py

from numpy import array
from basicLSTMs import get_stacked_lstm
from getSeqs import dataset

# split a multivariate sequence into samples
def split_sequences(sequences, n_steps_in, n_steps_out):
    X, y = list(), list()
    for i in range(len(sequences)):
        # find the end of this pattern
        end_ix = i + n_steps_in
        out_end_ix = end_ix + n_steps_out - 1
        # check if we are beyond the dataset
        if out_end_ix > len(sequences):
            break
        # gather input and output parts of the pattern
        seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix - 1:out_end_ix, -1]
        X.append(seq_x)
        y.append(seq_y)
    return array(X), array(y)


# choose a number of time steps
n_steps_in, n_steps_out = 3, 2
# covert into input/output
X, y = split_sequences(dataset, n_steps_in, n_steps_out)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = get_stacked_lstm(n_steps_in, n_features, n_steps_out)
# fit model
model.fit(X, y, epochs=200, verbose=1)
# demonstrate prediction
x_input = array([[70, 75], [80, 85], [90, 95]])
x_input = x_input.reshape((1, n_steps_in, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)