6 papers
ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting
Yichen Jiang, Yueqiao Chen, Dongyu Liu
State-of-the-art multivariate time-series forecasters can model complex temporal and cross-variable dependencies, yet their opaque representations provide limited insight into why…
DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings
Yuya Kawakami, Daniel Cayan, Dongyu Liu +2
Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal co…
ShapeTalk: Combining Natural Language and Sketch for Time-Series Pattern Querying
Guoruizhe Sun, Yueqiao Chen, Emily Guo +2
Searching for time-series segments that match user-defined patterns is important in domains such as finance, climate science, and healthcare. However, existing visual query tools o…
VEIL: How Visual Encoding Hijacking Induces Bias In Vision Models
Suranjana Sooraj, Xuyang Chen, Madhumitha Venkatesan +1
Rendering time series as chart images for CNN-based classification has become increasingly common in time-series classification (TSC). However, it remains unclear whether models le…
VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations
Madhumitha Venkatesan, Xuyang Chen, Dongyu Liu
Time-series classification (TSC) has advanced significantly with deep learning, yet most models rely solely on raw numerical inputs, overlooking alternative representations. While…
SigTime: Learning and Visually Explaining Time Series Signatures
Yu-Chia Huang, Juntong Chen, Dongyu Liu +1
Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering me…