arXiv:2606.15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular. Observations are asynchronous, missing values are informative rather than random, and sampling frequencies vary across sensors and operational windows. However, existing Time Series Question Answering (TSQA) benchmarks mostly assume regularly sampled inputs, leaving a fundamental gap in understanding how large language models (LLMs) and AI agents perform under irregular conditions. To bridge this gap, we introduce IRTS-ToolBench, a benchmark of 1,700 questions spanning 10 tas
The rapid advancement of large language models (LLMs) and the increasing demand for real-world AI applications highlight the immediate need to address irregularities in time series data, a common yet unaddressed challenge in existing benchmarks.
This development is crucial for advancing AI agents beyond academic settings, enabling them to handle complex, real-world data imperfections and operate effectively in critical applications.
The introduction of IRTS-ToolBench directly addresses a fundamental gap in AI agent evaluation, potentially leading to more robust and reliable AI systems capable of inferring from messy, uncleaned data encountered in practical deployments.
- · AI agents developers
- · Data scientists
- · Industries with irregular time series data (e.g., manufacturing, finance, health
- · AI models reliant on perfectly regular data
- · Existing time series benchmarks without irregular data considerations
More capable and trustworthy AI agents that function effectively with real-world, irregular time series data will emerge.
Improved AI agent performance in critical infrastructure and operational technology will reduce errors and enhance predictive maintenance or real-time decision-making.
The development could accelerate the deployment of deeply integrated AI agents across various sectors, leading to a significant increase in automation and operational efficiency.
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Read at arXiv cs.AI