arXiv:2509.26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models. Existing benchmarks often have limited domain coverage or overlook real-world settings such as tasks with covariates. Their aggregation procedures frequently lack statistical rigor, making it unclear whether observed performance differences reflect true improvements or random variation. Many benchmarks lack consistent evaluation infrastructure or are too rigid for integration into existing
The proliferation of pretrained models in AI makes robust and realistic benchmarks for time series forecasting increasingly critical for accurate evaluation and progress.
Improved benchmarks can accelerate AI development by providing more reliable performance indicators, fostering innovation in critical applications from finance to logistics.
The introduction of a 'realistic' benchmark will likely shift focus from theoretical performance to practical applicability, guiding future research and development in time series forecasting.
- · AI researchers
- · Data scientists
- · Industries relying on forecasting
- · Open-source AI benchmark providers
- · Laggard AI models
- · Benchmarks with poor statistical rigor
- · Companies relying on sub-optimal forecasting
- · Academic groups optimizing for narrow benchmarks
Researchers will adopt 'fev-bench' to validate new time series models against more realistic criteria.
This adoption will lead to the development of time series forecasting AI models that are genuinely more effective in real-world scenarios.
Improved real-world forecasting capabilities will enhance operational efficiencies and strategic decision-making across various industries, creating new economic value.
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Read at arXiv cs.LG