N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

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 long-horizon forecasting are the volatility of the predictions and their computational complexity. In this paper, 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, selectively emphasizing components with different frequencies and scales, while decomposing the input signal and synthesizing the forecast. We conduct an extensive empirical evaluation demonstrating the advantages of N-HiTS over the state-of-the-art long-horizon forecasting methods. On an array of multivariate forecasting tasks, the proposed method provides an average accuracy improvement of 25% over the latest Transformer architectures while reducing the computation time by an order of magnitude. Our code is available at https://github.com/cchallu/n-hits.