Papers by Mingtian Tan
Inferring Events from Time Series using Language Models (2026.acl-long)
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| Challenge: | Prior work on reasoning about time series in conjunction with natural language has largely overlooked event descriptions and focused on tasks involving just numeric data like trend analysis or anomaly detection. |
| Approach: | They propose a 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 benchmarking method. |
| Outcome: | The proposed method can infer unobserved events from time series data, even when providing minimal context. |
Language Models Still Struggle to Zero-shot Reason about Time Series (2024.findings-emnlp)
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| Challenge: | Time series are critical for decision-making in fields like finance and healthcare. |
| Approach: | They propose a framework for time series reasoning that includes formal tasks and a dataset of multi-scale time series paired with text captions across ten domains. |
| Outcome: | The proposed framework combines formal tasks and a dataset of multi-scale time series paired with text captions across ten domains to examine whether language models achieve three forms of reasoning. |