  Recent progress in neural forecasting accelerated improvements in the
performance of large-scale forecasting systems. Yet, long-horizon forecasting
remains a very difficult task. Two common challenges afflicting the task are
the volatility of the predictions and their computational complexity. We
introduce N-HiTS, a model which addresses both challenges by incorporating
novel hierarchical interpolation and multi-rate data sampling techniques. These
techniques enable the proposed method to assemble its predictions sequentially,
emphasizing components with different frequencies and scales while decomposing
the input signal and synthesizing the forecast. We prove that the hierarchical
interpolation technique can efficiently approximate arbitrarily long horizons
in the presence of smoothness. Additionally, we conduct extensive large-scale
dataset experiments from the long-horizon forecasting literature, demonstrating
the advantages of our method over the state-of-the-art methods, where N-HiTS
provides an average accuracy improvement of almost 20% over the latest
Transformer architectures while reducing the computation time by an order of
magnitude (50 times). Our code is available at bit.ly/3VA5DoT
