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 16% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). Our code is available at https://bit.ly/3JLIBp8.
翻译:神经预报在大规模预报系统运行方面最近的进展加快。然而,长视距预报仍然是一项非常困难的任务。影响这项任务的两个共同挑战是预测的波动及其计算复杂性。我们引入了N-HITS模型,通过采用新的等级内插和多率数据取样技术来应对这两个挑战。这些技术使得拟议的方法能够按顺序进行预测,强调不同频率和比例的元件,同时分解输入信号并合成预报。我们证明等级的内插技术可以在平稳的情况下有效地接近任意的长视线。此外,我们从长象预报文献中进行了广泛的大规模数据集实验,展示了我们的方法相对于最先进的方法的优势。 N-HITS提供了比最新变异结构平均精度提高16%的精度,同时将计算时间缩短到一个数量级(50倍 ) 。我们的代码可以在 https://bit.ly/3JLIBP8 上查阅 。