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arXiv · 1703.10089

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

Yagmur G. Cinar·Hamid Mirisaee·Parantapa Goswami·Eric Gaussier·Ali Ait-Bachir·Vadim Strijov
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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.

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Yagmur G. Cinar, Hamid Mirisaee, Parantapa Goswami, Eric Gaussier, Ali Ait-Bachir, Vadim Strijov. 2017-08-21. Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs. https://arxiv.org/abs/1703.10089

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