论文标题
基于差异注意的误差校正LSTM模型的时间序列预测
Difference Attention Based Error Correction LSTM Model for Time Series Prediction
论文作者
论文摘要
在本文中，我们为时间序列预测提出了一个新型模型，在该模型中，分别采用和组合了差异性LSTM模型和误差校正LSTM模型。虽然注意力集中的LSTM模型引入了差异功能，以在传统LSTM中表现出注意力，以专注于时间序列的明显变化。误差校正LSTM模型完善了差异注意LSTM模型的预测误差，以进一步提高预测准确性。最后，我们设计了一种培训策略，以同时共同培训这两个模型。有了其他差异功能和新的原理学习框架，我们的模型可以提高时间序列的预测准确性。进行各种时间序列的实验以证明我们方法的有效性。
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.
