3 papers
cs.LG2026
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz +3
State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. W…
cs.LG2025
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Christian Kloetergens +3
Probabilistic forecasting models for joint distributions of targets in irregular time series with missing values are a heavily under-researched area in machine learning, with, to t…
cs.LG2025
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born +1
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.…