arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can infer natural language events associated with time series data. We introduce an automated method for generating tasks that test a model's ability to reason about events associated with time series data based on sports data, and develop a new benchmarking method. In experiments spanning 18 LLMs, we prompt LLMs to infer unobserved events given time series data and observe surprising successes, even
The increasing sophistication of Large Language Models (LLMs) and their integration with diverse data types like time series allows for novel applications in event inference and causal reasoning, pushing the boundaries of AI capabilities.
This development indicates a significant leap in AI's ability to interpret complex, dynamic data, potentially automating event detection and causal analysis across numerous domains, from finance to operational intelligence.
LLMs are no longer limited to language generation and understanding but can now effectively infer unobserved real-world events from numerical time series data, expanding their analytical utility.
- · AI developers
- · Data scientists
- · Sports analytics
- · Predictive maintenance industries
- · Manual event analysis services
- · Traditional statistical models
- · Companies relying on slow data interpretation
LLMs will be increasingly deployed for automated anomaly detection and root cause analysis in industrial and financial systems.
The ability to infer events from time series could lead to more accurate and proactive decision-making in autonomous systems and financial trading.
This could accelerate the development of synthetic data generation for event-driven scenarios, further improving AI training and simulation capabilities.
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Read at arXiv cs.AI