7 citations · 9 across the 5 of their papers we have counts for
6 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…
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.…
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…
Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process
Sophia Sun, Wenyuan Chen, Zihao Zhou +3
Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are…
Automatic Integration for Spatiotemporal Neural Point Processes
Zihao Zhou, Rose Yu
Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point…
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,…