Period-aware content attention RNNs for time series forecasting with missing values

Recurrent neural networks (RNNs) recently received considerable attention for sequence modeling and time series analysis. Many time series contain periods, e.g. seasonal changes in weather time series or electricity usage at day and night time. Here, we first analyze the behavior of RNNs with an attention mechanism with respect to periods in time series and illustrate that they fail to model periods. Then, we propose an extended attention model for sequence-to-sequence RNNs designed to capture periods in time series with or without missing values. 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.