  In this paper, we propose a novel model for time series prediction in which
difference-attention LSTM model and error-correction LSTM model are
respectively employed and combined in a cascade way. While difference-attention
LSTM model introduces a difference feature to perform attention in traditional
LSTM to focus on the obvious changes in time series. Error-correction LSTM
model refines the prediction error of difference-attention LSTM model to
further improve the prediction accuracy. Finally, we design a training strategy
to jointly train the both models simultaneously. With additional difference
features and new principle learning framework, our model can improve the
prediction accuracy in time series. Experiments on various time series are
conducted to demonstrate the effectiveness of our method.
