| 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. |
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Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, Svitlana Vyetrenko
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