Enhancing electricity price forecasting accuracy: A novel filtering strategy for improved out-of-sample predictions
Authors:
- Andrea CerasaAndrea Cerasa
- Alessandro ZaniAZAlessandro Zani
Open Access
- 26 January 2025
- journal article
- Published by Elsevier in Applied Energy
- Vol. 383
Abstract
No abstract availableFunding Information
- European Commission Joint Research Centre
This publication has 66 references indexed in Scilit:
- Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkApplied Energy, 2021
- On the impact of outlier filtering on the electricity price forecasting accuracyApplied Energy, 2019
- Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithmsApplied Energy, 2018
- Anomaly detection by robust statisticsWIREs Data Mining and Knowledge Discovery, 2017
- Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationshipsComputational Statistics, 2014
- The Forecasting Performance of a Finite Mixture Regime‐Switching Model for Daily Electricity PricesJournal of Forecasting, 2014
- FSDA: A MATLAB toolbox for robust analysis and interactive data explorationChemometrics and Intelligent Laboratory Systems, 2012
- Efficient estimation of Markov regime-switching models: An application to electricity spot pricesAStA Advances in Statistical Analysis, 2011
- Forecasting time series with outliersJournal of Forecasting, 1993
- The effect of additive outliers on the forecasts from ARIMA modelsInternational Journal of Forecasting, 1989