1 citations · 1 across the 6 of their papers we have counts for
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Forecast Collapse in Time-Series Foundation Models
Shu Wan, Miles Ma, Hank Zhu +4
When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-section…
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu +1
Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is obse…
CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
Shu Wan, Reepal Shah, John Sabo +2
Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook und…
Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
Ahmet Kapkiç, Pratanu Mandal, Shu Wan +5
While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poo…