N-BEATS: Neural Basis Expansion Analysis For Interpretable Time Series Forecasting

By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados (Element AI), and Yoshua Bengio (MILA), 2019

This paper presents a block-based deep neural architecture for univariate time series point forecasting that is similar to very deep models (e.g. ResNet) used in more common deep learning applications such as image recognition. Furthermore, the authors demonstrate how their approach can be used to build predictive models that are interpretable.

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