[2509.15394] VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting

Computer Science > Machine Learning arXiv:2509.15394 (cs) [Submitted on 18 Sep 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting Authors:Weibin Feng, Ran Tao, John Cartlidge, Jin Zheng View a PDF of the paper titled VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting, by Weibin Feng and 3 other authors View PDF HTML (experimental) Abstract:Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal patterns crucial. Decomposition techniques make such structure explicit and thereby improve predictive performance. Variational Mode Decomposition (VMD) is a powerful signal-processing method for periodicity-aware decomposition and has seen growing adoption in recent years. However, existing studies often suffer from information leakage and rely on inappropriate hyperparameter tuning. To address these issues, we propose VMDNet, a causality-preserving framework that (i) applies sample-wise VMD to avoid temporal leakage; (ii) represents each decomposed mode with frequency-aware embeddings and decodes it using parallel temporal convolutional networks (TCNs), ensuring mode independence and efficient learning; and (iii) introduces a Stackelberg game inspired bilevel scheme to guide the selection of VMD's two key hyperparamet...