3 citations · 4 across the 8 of their papers we have counts for
8 papers
Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables
Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova +7
In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load;…
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…
Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility
Willa Potosnak, Malcolm Wolff, Mengfei Cao +6
While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs). Even…
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…
CLOVER : Probabilistic Forecasting with Coherent Learning Objective Reparameterization
Kin G. Olivares, Geoffrey Négiar, Ruijun Ma +3
Obtaining accurate probabilistic forecasts is an operational challenge in many applications, such as energy management, climate forecasting, supply chain planning, and resource all…
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Kin G. Olivares, O. Nganba Meetei, Ruijun Ma +3
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are…