Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
arXiv:2606.18367v1 Announce Type: new Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating regimes. We introduce regime-stratified evaluation and apply it to three TSFMs on two standard traffic speed benchmarks. Traffic exhibits abrupt regime switching between free-flow and congested states, producing bimodal speed distributions during transitions. When we stratify by traffic regime, both accuracy and prediction-interval coverage degrade sharply during transitions: transition-regime MAE re
The proliferation of Time Series Foundation Models (TSFMs) in critical applications highlights the urgent need for robust evaluation methods beyond aggregate metrics.
This research reveals critical shortcomings in current TSFM benchmarks, indicating that models may fail severely in real-world, dynamic conditions, impacting their deployability in sensitive applications.
The standard approach to evaluating TSFMs shifts from aggregate metrics to regime-stratified analysis, exposing performance degradation during critical transitions previously hidden.
- · Researchers developing rigorous evaluation methodologies for AI
- · Developers focused on robustness and safety in AI systems
- · Sectors with high-stakes dynamic time series data
- · AI models that perform well on aggregate metrics but poorly in transitional regi
- · Organizations relying solely on simplified TSFM benchmarks
- · Developers solely focused on improving average model performance
New benchmarks and evaluation frameworks will emerge, emphasizing regime-specific performance for time series models.
There will be increased demand for TSFMs that explicitly address regime-dependent failure modes and exhibit robustness across diverse operational states.
This could lead to a re-evaluation of deployment strategies for AI in critical infrastructure, where transitional states are common and failures have significant consequences.
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Read at arXiv cs.LG