DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

作者：

Highlights：

• We propose DSTP-RNN and DSTP-RNN-Ⅱ for long-term time series prediction.

• We enhance the attention to spatio-temporal relationships of time series.

• We study the deep spatial attention mechanism and give the interpretation.

• Our methods outperform nine baseline methods on four datasets.

摘要

•We propose DSTP-RNN and DSTP-RNN-Ⅱ for long-term time series prediction.•We enhance the attention to spatio-temporal relationships of time series.•We study the deep spatial attention mechanism and give the interpretation.•Our methods outperform nine baseline methods on four datasets.

论文关键词：Time series prediction,Spatio-temporal relationship,Attention mechanism,Dual-stage two-phase model,Deep attention network

论文评审过程：Received 12 April 2019, Revised 28 August 2019, Accepted 8 November 2019, Available online 8 November 2019, Version of Record 18 November 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113082