25 citations · 25 across the 5 of their papers we have counts for
6 papers
SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
Mingtian Tan, Palash Goyal, Mihir Parmar +6
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insuffi…
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remains compromised by pre-training data cont…
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,…
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…
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…