Description

This Master Prompt examines integrated forecasting frameworks that combine statistical methods, machine learning, deep learning, signal decomposition, mathematical optimisation, and domain-specific knowledge to improve forecasting performance and robustness. It covers hybrid statistical–neural architectures, decomposition–forecast–reconstruction pipelines, wavelet transforms, empirical mode decomposition, variational mode decomposition, Fourier and spectral analysis, trend–fluctuation separation, residual modelling, multiscale feature extraction, and multi-branch forecasting networks. The theme investigates ensemble learning, forecast averaging, weighted combinations, stacking, mixture-of-experts models, adaptive model selection, meta-learning, and optimisation-assisted forecasting. It also addresses decomposition leakage, information preservation, error propagation, computational complexity, ablation studies, and comparative evaluation against individual forecasting models. Particular attention is given to constructing theoretically justified hybrid systems that integrate complementary modelling capabilities rather than increasing complexity without demonstrated forecasting improvements.