1 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
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.…
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework
Minxuan Duan, Yinlong Qian, Lingyi Zhao +4
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data…
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…