Multiresolutional statistical machine learning for testing interdependence of power markets: A Variational Mode Decomposition-based approach
作者：
Highlights：
• We investigate the multiscale predictability between European energy markets.
• A variation mode decomposition-based statistical methodology is defined and used.
• The method allows extracting the main hidden patterns between time series.
• A novel multiscaled neuro-autoregressive model is designed and effectively applied.
• The results reveal a dependence across long- and medium-run investment time horizons.
摘要
•We investigate the multiscale predictability between European energy markets.•A variation mode decomposition-based statistical methodology is defined and used.•The method allows extracting the main hidden patterns between time series.•A novel multiscaled neuro-autoregressive model is designed and effectively applied.•The results reveal a dependence across long- and medium-run investment time horizons.
论文关键词：Variational Mode Decomposition,Multiscaled cross-correlation analysis,Multiscale causality,Multiscaled Neural Network,Energy exchange,COVID-19
论文评审过程：Received 10 May 2021, Revised 11 June 2022, Accepted 9 July 2022, Available online 16 July 2022, Version of Record 23 July 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118161
