4 citations · 5 across the 4 of their papers we have counts for
6 papers
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
Muhammad Hasan Ferdous, Ian Stockwell, 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…
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…
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
Emam Hossain, Muhammad Hasan Ferdous, Jianwu Wang +2
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, whi…
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this s…
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Time series data are found in many areas of healthcare such as medical time series, electronic health records (EHR), measurements of vitals, and wearable devices. Causal discovery,…