4 papers · 1 filter
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…
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…
Spatio-temporal Causal Learning for Streamflow Forecasting
Shu Wan, Reepal Shah, Qi Deng +3
Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establi…
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…