We propose here an extended attention model for sequence-to-sequencerecurrent neural networks (RNNs) designed to capture (pseudo-)periods in timeseries. This extended attention model can be deployed on top of any RNN and isshown to yield state-of-the-art performance for time series forecasting onseveral univariate and multivariate time series.
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