# Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

arXiv: https://arxiv.org/abs/2506.08113 · HTML: https://mlsanity.com/paper/2506.08113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks](https://mlsanity.com/paper/1805.06649/) (2018)
- [Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting](https://mlsanity.com/paper/2410.09487/) (2024)
- [Financial Fine-tuning a Large Time Series Model](https://mlsanity.com/paper/2412.09880/) (2024)
- [Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods](https://mlsanity.com/paper/2411.03372/) (2024)
- [Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation](https://mlsanity.com/paper/2501.07276/) (2025)

## Built on by (most cited first, 5 of 5)

- [FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting](https://mlsanity.com/paper/2604.22328/) (2026)
- [Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?](https://mlsanity.com/paper/2609.00089/) (2026)
- [Tiny-TSM: Efficiently Training a Lightweight SOTA Time Series Foundation Model](https://mlsanity.com/paper/2511.19272/) (2025)
- [A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches](https://mlsanity.com/paper/2512.09360/) (2025)
- [Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium](https://mlsanity.com/paper/2605.17045/) (2026)

## Abstract (arXiv)

Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods for EPF. Using 2024 day-ahead auction (DAA) electricity prices from Germany, France, the Netherlands, Austria, and Belgium, we generate daily forecasts with a one-day horizon. Chronos-Bolt and Time-MoE emerge as the strongest among the TSFMs, performing on par with traditional models. However, the biseasonal MSTL model, which captures daily and weekly seasonality, stands out for its consistent performance across countries and evaluation metrics, with no TSFM statistically outperforming it.