1 citations · 2 across the 20 of their papers we have counts for
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Rethinking Multimodal Time-Series Forecasting Evaluation
Haoxin Liu, Yichen Zhou, Rajat Sen +2
We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world time series with diverse doma…
Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
Haoxin Liu, Yichen Zhou, Rajat Sen +2
Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, but unlike LLMs they cannot consume multimodal, non-numerical context that often…
Seeking Universal Shot Language Understanding Solutions
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao +2
Shot language understanding (SLU) is crucial for cinematic analysis but remains challenging due to its diverse cinematographic dimensions and subjective expert judgment. While visi…
Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations
Harshavardhan Kamarthi, Shangqing Xu, Xinjie Tong +4
Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significant accuracy and scalability…
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Shangqing Xu, Harshavardhan Kamarthi, Haoxin Liu +1
Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-…
Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction
Shangqing Xu, Zhiyuan Zhao, Megha Sharma +4
Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing…