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
Code: https://github.com/OberMarco/Energy_Benchmark_TSFM_pub
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
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