arXiv:2606.18539v1 Announce Type: new Abstract: Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.g., average error) on clean held-out data, under the implicit assumption that it predicts deployed reliability. However, real faults are not i.i.d noise but structured events with temporal shape, broken cross-variable dependencies, regime change coupled with missingness, and causal propagation across a sensing pipeline. Treating TSF robustness as a data-quality probl
The increasing reliance on time series forecasting in critical sectors demands more robust evaluation methods than current industry practices provide, especially as AI models are deployed in real-world, noisy environments.
This research highlights a fundamental gap in how AI models for time series forecasting are benchmarked, potentially leading to widespread deployment of unreliable systems in critical infrastructure and finance.
The focus for evaluating time series forecast models will shift from simplistic aggregate error metrics to more sophisticated assessments of robustness against structured, real-world faults.
- · Developers of robust AI models
- · Sectors reliant on accurate forecasting (energy, finance, healthcare)
- · AI assurance and testing platforms
- · Models optimized only for 'clean' data
- · Users relying on simplistic benchmarking
- · Businesses with brittle forecasting systems
New benchmarks and methodologies for evaluating time series forecasting models will emerge, prioritizing resilience to structural faults.
There will be increased demand for AI models that can explicitly handle and adapt to temporal irregularities, missing data, and regime changes.
Regulation of AI systems in critical infrastructure may incorporate robustness against structured faults as a key compliance requirement.
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Read at arXiv cs.LG