4 citations · 11 across the 22 of their papers we have counts for
4 papers · 2 filters
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…
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann +1
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challe…
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi +3
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as wi…
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.…