Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs

Yagmur Gizem Cinar, Hamid Mirisaee, Parantapa Goswami, Éric Gaussier, Ali Aït-Bachir, Vadim V. Strijov

Abstract: 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.