# Difference Attention Based Error Correction LSTM Model for Time Series Prediction

arXiv: https://arxiv.org/abs/2003.13616 · HTML: https://mlsanity.com/paper/2003.13616/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Reviving Error Correction in Modern Deep Time-Series Forecasting](https://mlsanity.com/paper/2605.21088/) (2026)
- [A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting](https://mlsanity.com/paper/2202.11402/) (2022)

## Abstract (arXiv)

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.