  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 wide array of target domains, and fast to
train. We test the proposed architecture on several well-known datasets,
including M3, M4 and TOURISM competition datasets containing time series from
diverse domains. We demonstrate state-of-the-art performance for two
configurations of N-BEATS for all the datasets, improving forecast accuracy by
11% over a statistical benchmark and by 3% over last year's winner of the M4
competition, a domain-adjusted hand-crafted hybrid between neural network and
statistical time series models. The first configuration of our model does not
employ any time-series-specific components and its performance on heterogeneous
datasets strongly suggests that, contrarily to received wisdom, deep learning
primitives such as residual blocks are by themselves sufficient to solve a wide
range of forecasting problems. Finally, we demonstrate how the proposed
architecture can be augmented to provide outputs that are interpretable without
considerable loss in accuracy.
