FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
cs.LG, cs.AI, cs.CE
Submitted: 2026-04-24
Updated: 2026-07-17
Journal ref: Energy and AI, 2026, 100867, ISSN 2666-5468
DOI: 10.1016/j.egyai.2026.100867
Code: https://github.com/OberMarco/Energy_Benchmark_TSFM_pub
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Chronos-2: From Univariate to Universal Forecasting
- GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
- fev-bench: A Realistic Benchmark for Time Series Forecasting
- Time Series Foundation Models for Energy Load Forecasting on Consumer Hardware: A Multi-Dimensional Zero-Shot Benchmark
- Foundation Models for Clean Energy Forecasting: A Comprehensive Review
- Moirai 2.0: When Less Is More for Time Series Forecasting
- Toto 2.0: Time Series Forecasting Enters the Scaling Era
- FlowState: Sampling-Rate-Equivariant Time-Series Forecasting
- TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
- TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
- TS-Arena -- A Live Forecast Pre-Registration Platform
- Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years
- Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
- Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit
- Beyond IID: How General Are Tabular Foundation Models, Really?
- Conformalized Quantile Regression
- Deep learning in bioinformatics: introduction, application, and perspective in big data era
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks