Regression markets and application to energy forecasting
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DOI: 10.1007/s11750-022-00631-7
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- Gal-Or, Esther, 1985. "Information Sharing in Oligopoly," Econometrica, Econometric Society, vol. 53(2), pages 329-343, March.
- Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
- Messner, Jakob W. & Pinson, Pierre, 2019. "Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1485-1498.
- P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
- Gonçalves, Carla & Bessa, Ricardo J. & Pinson, Pierre, 2021. "A critical overview of privacy-preserving approaches for collaborative forecasting," International Journal of Forecasting, Elsevier, vol. 37(1), pages 322-342.
- Dirk Bergemann & Alessandro Bonatti, 2019.
"Markets for Information: An Introduction,"
Annual Review of Economics, Annual Reviews, vol. 11(1), pages 85-107, August.
- Dirk Bergemann & Alessandro Bonatti, 2018. "Markets for Information: An Introduction," Cowles Foundation Discussion Papers 2142, Cowles Foundation for Research in Economics, Yale University.
- Bergemann, Dirk & Bonatti, Alessandro, 2018. "Markets for Information: An Introduction," CEPR Discussion Papers 13148, C.E.P.R. Discussion Papers.
- Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
- Sommer, Benedikt & Pinson, Pierre & Messner, Jakob W. & Obst, David, 2021. "Online distributed learning in wind power forecasting," International Journal of Forecasting, Elsevier, vol. 37(1), pages 205-223.
- Mohammad Rasouli & Michael I. Jordan, 2021. "Data Sharing Markets," Papers 2107.08630, arXiv.org, revised Jul 2021.
- Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
- Winter, Eyal, 2002. "The shapley value," Handbook of Game Theory with Economic Applications, in: R.J. Aumann & S. Hart (ed.), Handbook of Game Theory with Economic Applications, edition 1, volume 3, chapter 53, pages 2025-2054, Elsevier.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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- Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
- VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.
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Keywords
Energy forecasting; Data markets; Mechanism design; Regression; Estimation;All these keywords.
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