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Advancing Economic Forecasting With Hybrid Time Series and Functional Models: Evidence From Electricity Demand Data

Year: 2025

Type: article

Abstract: Accurate short-term electricity demand forecasting (STDF) is critical not only for power system operational dependability but also for guiding strategic energy economic decision-making and promoting resilient, sustainable energy transitions. STDF informs market pricing, capacity planning, and regulatory policy by reducing forecasting uncertainty and enabling optimal resource allocation. This paper presents a novel hybrid forecasting framework that utilizes functional time series (FTS) modeling combined with advanced statistical anomaly detection methods to enhance forecasting accuracy in volatile power markets. We assess the performance of a variety of forecasting methods, including functional autoregressive (FAR), vector autoregressive (VAR), autoregressive moving average (ARMA), and machine learning models like autoregressive neural networks (ARNN) and k-nearest neighbors (KNN), in capturing complex temporal dependencies and structural dynamics of electricity demand. A key methodological novelty is the use of anomaly-aware preprocessing, which detects and corrects exogenous demand shocks that frequently distort time series models, particularly in deregulated or volatile energy markets. The FTS technique provides a concise yet expressive depiction of intra-day demand curves, allowing for both smooth functional variation and abrupt regime transitions. Empirical validation using high-frequency real-world data demonstrates that the proposed hybrid FTS framework consistently outperforms standard statistical and machine learning techniques in terms of prediction accuracy and resilience. This study makes a significant contribution to the literature on economic forecasting under uncertainty by combining advanced time series modeling with the demands of modern electricity markets. It emphasizes the need to integrate statistical rigor with machine learning flexibility to enhance predictive reliability, facilitate data-driven energy economics, and inform critical policy and investment decisions for long-term transitions.

Source: IEEE Access

DOI: 10.1109/access.2025.3631725

Authors: Faheem Jan, Hasnain Iftikhar, Naveed Gul, Paulo Canas Rodrigues, Sahar Shah, Mohd Ziaur Rehman