4 papers
CaTS-Bench: Can Language Models Describe Time Series?
Luca Zhou, Pratham Yashwante, Marshall Fisher +4
Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation, and contextual understanding. Existin…
Can LLMs Understand Time Series Anomalies?
Zihao Zhou, Rose Yu
Large Language Models (LLMs) have gained popularity in time series forecasting, but their potential for anomaly detection remains largely unexplored. Our study investigates whether…
Neural Point Process for Learning Spatiotemporal Event Dynamics
Zihao Zhou, Xingyi Yang, Ryan Rossi +2
Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However,…
Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
Kai Kim, Howard Tsai, Rajat Sen +5
Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset.…