Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs
arXiv:1703.10089v281 citations
Originality Incremental advance
AI Analysis
This addresses forecasting accuracy for time series data, but appears incremental as it builds on existing attention mechanisms.
The authors tackled the problem of capturing pseudo-periods in time series forecasting by proposing an extended attention model for sequence-to-sequence RNNs, achieving state-of-the-art performance on several univariate and multivariate time series.
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.