ENERGIES_14_3249_PYTHON: Market data and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in Jedrzejewski et al. (2021) Energies 14, 3249

Arkadiusz Jędrzejewski, Grzegorz Marcjasz and Rafał Weron

WORMS Software (WORking papers in Management Science Software) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: This ZIP file contains power market data (CSV files) and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in A. Jedrzejewski, G. Marcjasz, R. Weron (2021) Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO, Energies 14(11), 3249 (http://dx.doi.org/10.3390/en14113249).

Language: PYTHON
Requires: PYTHON.
Keywords: Electricity price forecasting; Day-ahead market; LASSO; Long-term seasonal component; Variance stabilizing transformation; Forecast averaging (search for similar items in EconPapers)
Date: 2021-07-30
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https://worms.pwr.edu.pl/RePEc/ahh/wcodes/Energies_14_3249_Python.zip Zipped file (application/zip)

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