Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations

Disciplines :

Management information systems
Computer science

Author, co-author :

HORNEK, Timothée ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX

SARTIPI, Amir ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX

TCHAPPI HAMAN, Igor ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX

FRIDGEN, Gilbert ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX

External co-authors :

Language :

English

Title :

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

Publication date :

27 May 2025

Event name :

2025 21st International Conference on the European Energy Market (EEM)

Event organizer :

Polytechnic University of Lisbon

Event place :

Lisbon, Portugal

Event date :

from 27 to 29 May 2025

Event number :

Audience :

International

Main work title :

2025 21st International Conference on the European Energy Market (EEM)

Publisher :

IEEE

ISBN/EAN :

979-8-3315-1279-8

Pages :

1-7

Peer reviewed :

Peer reviewed

Focus Area :

Security, Reliability and Trust

Development Goals :

9. Industry, innovation and infrastructure

Additional URL :

http://xplorestaging.ieee.org/ielx8/11050076/11050077/11050326.pdf?arnumber=11050326

FnR Project :

FNR13342933 - DFS - Paypal-fnr Pearl Chair In Digital Financial Services, 2019 (01/01/2020-31/12/2024) - Gilbert Fridgen

Name of the research project :

U-AGR-8168 - ENOVOS Finatrax - FRIDGEN Gilbert

Funders :

Enovos Luxembourg S.A.

Funding text :

This research was funded in part by the Luxembourg National Research Fund (FNR) and PayPal, PEARL grant reference 13342933/Gilbert Fridgen. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission. The research was carried out as part of a partnership with the energy retailer Enovos Luxembourg S.A.

Available on ORBilu :

since 11 July 2025

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Bibliography

G. Woo, C. Liu, A. Kumar, C. Xiong, S. Savarese, and D. Sahoo, "Unified Training of Universal Time Series Forecasting Transformers," May 2024, arXiv:2402.02592 [cs]. [Online]. Available: http://arxiv.org/ abs/2402.02592
R. Weron, "Electricity price forecasting: A review of the state-of-The-art with a look into the future," International Journal of Forecasting, vol. 30, no. 4, pp. 1030-1081, Oct. 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169207014001083
European Commission, "Consolidated text: Commission Regulation (EU) 2015/1222 of 24 July 2015 establishing a guideline on capacity allocation and congestion management (Text with EEA relevance)," 2021. [Online]. Available: http://data.europa.eu/eli/reg/2015/1222/202 1-03-15
A. F. Ansari, L. Stella, C. Turkmen, X. Zhang, P. Mercado, H. Shen, O. Shchur, S. S. Rangapuram, S. P. Arango, S. Kapoor, J. Zschiegner, D. C. Maddix, H. Wang, M. W. Mahoney, K. Torkkola, A. G. Wilson, M. Bohlke-Schneider, and Y. Wang, "Chronos: Learning the Language of Time Series," Nov. 2024, arXiv:2403.07815 [cs]. [Online]. Available: http://arxiv.org/abs/2403.07815
A. Das, W. Kong, R. Sen, and Y. Zhou, "A decoder-only foundation model for time-series forecasting," Apr. 2024, arXiv:2310.10688 [cs]. [Online]. Available: http://arxiv.org/abs/2310.10688
X. Shi, S. Wang, Y. Nie, D. Li, Z. Ye, Q. Wen, and M. Jin, "Time- MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts," Oct. 2024, arXiv:2409.16040 [cs]. [Online]. Available: http://arxiv.org/abs/2409.16040
A. Garza, C. Challu, and M. Mergenthaler-Canseco, "TimeGPT- 1," May 2024, arXiv:2310.03589 [cs]. [Online]. Available: http: //arxiv.org/abs/2310.03589
X. Fu, M. Hirano, and K. Imajo, "Financial Fine-tuning a Large Time Series Model," Dec. 2024, arXiv:2412.09880 [q-fin]. [Online]. Available: http://arxiv.org/abs/2412.09880
M. Meyer, D. Zapata, S. Kaltenpoth, and O. Muller, "Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting," Oct. 2024, arXiv:2410.09487 [cs]. [Online]. Available: http://arxiv.org/abs/2410.09487
A. Sartipi, J. D. Fernandez, S. P. Menci, and A. Magitteri, "Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation," Jan. 2025, arXiv:2501.07276 [cs]. [Online]. Available: http://arxiv.org/abs/2501.0 7276
X. Lu, J. Qiu, Y. Yang, C. Zhang, J. Lin, and S. An, "Large Language Model-based Bidding Behavior Agent and Market Sentiment Agent-Assisted Electricity Price Prediction," Policy and Regulation IEEE Transactions on Energy Markets, pp. 1-13, 2024, conference Name: Policy and Regulation IEEE Transactions on Energy Markets. [Online]. Available: https://ieeexplore.ieee.org/document/10804210/?a rnumber=10804210
A.-V. Andrei, G. Velev, F.-M. Toma, D. T. Pele, and S. Lessmann, "Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods," Nov. 2024, arXiv:2411.03372 [cs]. [Online]. Available: http://arxiv.org/abs/2411.03372
F. Diebold and R. Mariano, "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, vol. 13, no. 3, pp. 253-63, 1995, publisher: American Statistical Association. [Online]. Available: https://econpapers.repec.org/article/besjnlbes/v 3a13 3ay 3a1995 3ai 3a3 3ap 3a253-63.htm
F. Garza, M. M. Canseco, C. Challu, and K. G. Olivares, "StatsForecast: Lightning fast forecasting with statistical and econometric models," PyCon: Salt Lake City, UT, USA, 2022.
K. Bandara, R. J. Hyndman, and C. Bergmeir, "MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns," Jul. 2021. [Online]. Available: https: //arxiv.org/abs/2107.13462v1
A. M. De Livera, R. J. Hyndman, and R. D. Snyder, "Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing," Journal of the American Statistical Association, vol. 106, no. 496, pp. 1513-1527, Dec. 2011, publisher: ASA Website eprint: https://doi.org/10.1198/jasa.2011.tm09771. [Online]. Available: https://doi.org/10.1198/jasa.2011.tm09771
B. Tyler, "MFLES," 2024. [Online]. Available: https://github.com/tbl ume1992/MFLES
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825-2830, 2011. [Online]. Available: http://jmlr.org/papers/v12/pedregosa11a.html
G. James, D. Witten, T. Hastie, R. Tibshirani, and others, An introduction to statistical learning. Springer, 2013, vol. 112.
V. Vapnik, "Statistical learning theory," John Wiley & Sons, vol. 2, pp. 831-842, 1998.
F. Ziel and R. Weron, "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, vol. 70, pp. 396-420, Feb. 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S014098831730436X
ENTSO-E, "Bidding Zone Technical Report 2021," European Network of Transmission System Operators for Electricity, Tech. Rep., 2021. [Online]. Available: https://www.entsoe.eu/news/2021/11/18/entso-e-p ublishes-its-2021-bidding-zone-technical-report-providing-A-transpare nt-and-factual-information-on-grid-congestions-in-The-eu/
B. Uniejewski, R. Weron, and F. Ziel, "Variance Stabilizing Transformations for Electricity Spot Price Forecasting," IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 2219-2229, Mar. 2018, conference Name: IEEE Transactions on Power Systems.