Improving LSTM Time-series Predictions
I have just used LSTM to train a model predict time series value and get good result as below:

# 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]))

model = Sequential()
model.add(LSTM(40, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=0)


Here is my result: blue is real value, orange is predict.