Variational Hierarchical N-BEATS Model for Long-Term Time-Series Forecasting

Long-term time-series forecasting (LTSF) is gaining increasing attention due to its significant challenges and real-world applications. However, existing studies underexplore the role of hierarchical timestamp information in LTSF. We find this information crucial, as neglecting it may lead to the loss of broader perspectives necessary for understanding hierarchical effects, such as weekly and yearly patterns. Therefore, we propose VH-NBEATS, an interpretable variational hierarchical model that extends the N-BEATS architecture to address the challenges outlined above. VH-NBEATS consists of two blocks: the hierarchical timestamp block and the harmonic seasonal block, which are designed to capture hierarchical seasonal and trending effects. To tackle the high variability often observed in time series, VH-NBEATS incorporates a variational autoencoder (VAE), significantly enhancing the standard deterministic approach. The experimental results are evaluated on seven real-world datasets, demonstrating state-of-the-art (SOTA) performance for LTSF. We also prove that the hierarchical timestamp block can enable plug-and-play with any methods, such as PatchTST, Informer, and DLinear, for better performance.