Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)

Although there has been a good amount of work from DL to tackle time series forecasting, very little focus has been on long-horizon forecasting. Despite recent progress, long-horizon forecasting remains a challenge because of two reasons:

• The expressiveness required to truly capture the variation
• The computational complexity

Attention-based methods (Transformers) and N-BEATS-like methods scale quadratically in memory and the computational cost concerning the forecasting horizon.

The authors claim that N-HiTS drastically cuts long-forecasting compute costs while simultaneously showing 25% accuracy improvements compared to existing Transformer-based architectures across a large array of multi-variate forecasting datasets.

Reference check

The research paper by Challu et al. on N-HiTS is cited in the References section as 5.

The Architecture of N-HiTS

N-HiTS can be considered as an...