使用Bidirectional LSTM 做时间序列进行预测

使用Bidirectional LSTM 做时间序列进行预测

univariate bidirectional lstm example

from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Bidirectional

split a univariate sequence

def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)

define input sequence

raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]

choose a number of time steps

n_steps = 3

split into samples

X, y = split_sequence(raw_seq, n_steps)

reshape from [samples, timesteps] into [samples, timesteps, features]

n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))

define model

model = Sequential()
model.add(Bidirectional(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([70, 80, 90])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)