Multivariate LSTM Models_Multiple Input Series

Multivariate_LSTM_Multiple_Input_Series.py

# https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
# multivariate lstm example
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense

# split a multivariate sequence into samples
def split_sequences(sequences, n_steps):
	X, y = list(), list()
	for i in range(len(sequences)):
		# find the end of this pattern
		end_ix = i + n_steps
		# check if we are beyond the dataset
		if 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, -1]
		X.append(seq_x)
		y.append(seq_y)
	return array(X), array(y)

# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps = 3
# convert into input/output
X, y = split_sequences(dataset, n_steps)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([[80, 85], [90, 95], [100, 105]])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)