4 papers
Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma +5
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step look…
SPADE-S: A Sparsity-Robust Foundational Forecaster
Malcolm Wolff, Matthew Li, Ravi Kiran Selvam +11
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging fo…
End-to-End Probabilistic Framework for Learning with Hard Constraints
Utkarsh Utkarsh, Danielle C. Maddix, Ruijun Ma +2
We present ProbHardE2E, a probabilistic forecasting framework that incorporates hard operational/physical constraints, and provides uncertainty quantification. Our methodology uses…
BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
Ruifeng Tan, Weixiang Hong, Jiayue Tang +6
Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite imp…