2021
DOI: 10.48550/arxiv.2111.03418
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Meta-Forecasting by combining Global Deep Representations with Local Adaptation

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 introduc… Show more

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