NHITS: 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 the task are the volatility of the predictions and their computational complexity. We introduce NHITS, 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…

• NS
  National Science Foundation

  Award: 2038612
• NA
  National Aeronautics and Space Administration
• UD
  U.S. Department of Homeland Security

  Award: 18DN-ARI-00031
• DA
  Defense Advanced Research Projects Agency

  Awards: FA8750-17-2-0130, FA8750
• AR
  Advanced Research Projects Agency