Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LIG（格勒诺布尔信息实验室）；Viseo R&D（Viseo 研发部）；Coservit（Coservit 公司）；CCRAS（俄罗斯科学院计算中心）
英文摘要：
We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
