HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling
This addresses the challenge of multi-scale temporal modeling with uncertainties for applications like energy consumption forecasting, representing a novel method for a known bottleneck.
The paper tackled the problem of temporal modeling in complex systems by proposing HierCVAE, which integrates hierarchical attention with conditional variational autoencoders, resulting in a 15-40% improvement in prediction accuracy and better uncertainty calibration compared to state-of-the-art methods.
Temporal modeling in complex systems requires capturing dependencies across multiple time scales while managing inherent uncertainties. We propose HierCVAE, a novel architecture that integrates hierarchical attention mechanisms with conditional variational autoencoders to address these challenges. HierCVAE employs a three-tier attention structure (local, global, cross-temporal) combined with multi-modal condition encoding to capture temporal, statistical, and trend information. The approach incorporates ResFormer blocks in the latent space and provides explicit uncertainty quantification via prediction heads. Through evaluations on energy consumption datasets, HierCVAE demonstrates a 15-40% improvement in prediction accuracy and superior uncertainty calibration compared to state-of-the-art methods, excelling in long-term forecasting and complex multi-variate dependencies.