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Update 166a-Intro_to_time_series_Forecasting_using_LSTM.py #74

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Update 166a-Intro_to_time_series_Forecasting_using_LSTM.py #74

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24 changes: 12 additions & 12 deletions 166a-Intro_to_time_series_Forecasting_using_LSTM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ def to_sequences(dataset, seq_size=1):
x = []
y = []

for i in range(len(dataset)-seq_size-1):
for i in range(len(dataset)-seq_size):
#print(i)
window = dataset[i:(i+seq_size), 0]
x.append(window)
Expand All @@ -82,12 +82,12 @@ def to_sequences(dataset, seq_size=1):

######################################################
# Reshape input to be [samples, time steps, features]
#trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
#testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
#trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))
#testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))
#
#print('Single LSTM with hidden Dense...')
#model = Sequential()
#model.add(LSTM(64, input_shape=(None, seq_size)))
#model.add(LSTM(64, input_shape=(seq_size,1)))
#model.add(Dense(32))
#model.add(Dense(1))
#model.compile(loss='mean_squared_error', optimizer='adam')
Expand All @@ -99,11 +99,11 @@ def to_sequences(dataset, seq_size=1):

#Stacked LSTM with 1 hidden dense layer
# reshape input to be [samples, time steps, features]
#trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
#testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
#trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))
#testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))
#
#model = Sequential()
#model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(None, seq_size)))
#model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(seq_size,1)))
#model.add(LSTM(50, activation='relu'))
#model.add(Dense(32))
#model.add(Dense(1))
Expand All @@ -115,14 +115,14 @@ def to_sequences(dataset, seq_size=1):

#Bidirectional LSTM
# reshape input to be [samples, time steps, features]
#trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
#testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
#trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))
#testX = np.reshape(testX, (testX.shape[0],testX.shape[1],1))
#
##For some sequence forecasting problems we may need LSTM to learn
## sequence in both forward and backward directions
#from keras.layers import Bidirectional
#model = Sequential()
#model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(None, seq_size)))
#model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(seq_size,1)))
#model.add(Dense(1))
#model.compile(optimizer='adam', loss='mean_squared_error')
#model.summary()
Expand Down Expand Up @@ -182,10 +182,10 @@ def to_sequences(dataset, seq_size=1):
# shift test predictions for plotting
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(seq_size*2)+1:len(dataset)-1, :] = testPredict
testPredictPlot[len(trainPredict)+(seq_size*2):len(dataset), :] = testPredict

# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
plt.show()