5 papers
Inferring Events from Time Series using Language Models
Mingtian Tan, Mike A. Merrill, Zack Gottesman +3
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 a…
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, several living benchmarks have b…
Toward Reasoning-Centric Time-Series Analysis
Xinlei Wang, Mingtian Tan, Jing Qiu +2
Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift,…
Are Language Models Actually Useful for Time Series Forecasting?
Mingtian Tan, Mike A. Merrill, Vinayak Gupta +2
Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent…
Language Models Still Struggle to Zero-shot Reason about Time Series
Mike A. Merrill, Mingtian Tan, Vinayak Gupta +2
Time series are critical for decision-making in fields like finance and healthcare. Their importance has driven a recent influx of works passing time series into language models, l…