7 papers
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
Muhammad Hasan Ferdous, Md Osman Gani
Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating ca…
Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
Emam Hossain, Muhammad Hasan Ferdous, Devon Dunmire +2
Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under distributio…
Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction
Emam Hossain, Md Osman Gani
Conventional machine learning and deep learning models typically rely on correlation-based learning, which often fails to distinguish genuine causal relationships from spurious ass…
TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery
Muhammad Hasan Ferdous, Emam Hossain, Md Osman Gani
Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships. However, such datasets remain scarce, and exist…
Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet
Emam Hossain, Md Osman Gani, Devon Dunmire +2
The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the…
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
Siyuan Wang, James R. Foulds, Md Osman Gani +1
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corrobo…