Meta-Forecasting by combining Global DeepRepresentations with Local Adaptation

• URL: http://arxiv.org/abs/2111.03418v1
• Date: Fri, 5 Nov 2021 11:45:02 GMT
• Title: Meta-Forecasting by combining Global DeepRepresentations with Local Adaptation
• Authors: Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias Seeger, Cedric Archambeau
• Abstract summary: We introduce a novel forecasting method called Meta Global-Local Auto-Regression (Meta-GLAR) It adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work.
• Score: 12.747008878068314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy. However, the accuracy of these methods suffers greatly when modeling out-of-sample time series, significantly limiting their applicability compared to classical forecasting methods. To bridge this gap, we adopt a meta-learning view of the time series forecasting problem. We introduce a novel forecasting method, called Meta Global-Local Auto-Regression (Meta-GLAR), that adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Crucially, the parameters ofthe RNN are learned across multiple time series by backpropagating through the closed-form adaptation mechanism. In our extensive empirical evaluation we show that our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work.

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