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 this http URL

本文提出 N-HiTS 模型，该模型可以通过采用新颖的分层插值和多速率数据采样技术解决了长时间预测中的波动性和计算复杂性问题，并证明了分层插值技术可以在存在光滑性时有效地逼近任意长的时间预测，在大规模数据集实验中表现优于现有的 Transformer 架构，平均准确率提高了近 20％，同时降低了一个数量级的计算时间（50 倍）。