arXiv:2602.12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation. However, we contend that existing benchmarks exhibit common limitations in four dimensions: constrained data composition dominated by reused legacy sources, compromised data integrity lacking rigorous quality assurance, misaligned task formulations detached from real-world contexts, and rigid analysis perspectives that obscure generalizable insights. To bridge these gaps, we introduce TIME, a nex
The proliferation of Time Series Foundation Models (TSFMs) highlights a critical need for updated, robust benchmarks to accurately evaluate their capabilities following rapid advancements in AI.
Improved benchmarks like TIME will accelerate the development and deployment of more reliable and generalizable AI models for forecasting across various strategic sectors, impacting decision-making and resource allocation.
The shift from specific dataset modeling to generalizable task evaluation will lead to more representative and rigorous testing of time series forecasting AI, revealing true model performance and limitations.
- · AI researchers in time series forecasting
- · Industries relying on time series predictions (e.g., finance, logistics, energy)
- · Developers of Time Series Foundation Models (TSFMs)
- · Legacy time series forecasting methods
- · AI models that perform poorly under rigorous, generalizable benchmarks
- · Organizations relying on outdated forecasting evaluation methods
The new TIME benchmark will enable more accurate comparisons and development of Time Series Foundation Models.
This improved evaluation will accelerate the practical application of TSFMs in critical infrastructure and economic sectors.
Enhanced forecasting capabilities could lead to more efficient resource management, supply chain optimization, and financial stability globally.
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