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Introduces an evolutionary neural architecture search framework (EvoTS) for discovering task-adaptive Transformer-like models for multivariate time-series forecasting. The approach uses a modular genome representation and achieves competitive performance on ETT benchmark datasets.
Introduces SpikF-GO, a spiking neural network model for multivariate time series forecasting that combines graph-based inter-variable dependency modeling with spike-driven spectral processing, achieving state-of-the-art results among SNN methods with reduced energy consumption.
Proposes PESD-TSF, a physics-inspired structured decomposition framework for long-term time series forecasting that addresses periodic perception degradation, trend-noise entanglement, and loss of cross-variable dependencies via multiplicative periodic gating, multi-scale structured encoder, and cross-scale collaborative attention.