N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

    Boris N. Oreshkin
    Boris N. Oreshkin
    Dmitri Carpov
    Dmitri Carpov
    Cited by: 0|Bibtex|29|

    international conference on learning representations, 2020.

    Abstract:

    We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a ...More
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