Search Results for author: Stefan Born

Found 7 papers, 2 papers with code

Probabilistic Forecasting of Irregular Time Series via Conditional Flows

no code implementations9 Feb 2024 Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme

Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.

Astronomy Irregular Time Series +1

Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries

no code implementations4 Dec 2023 Maxim Borisyak, Stefan Born, Peter Neubauer, Mariano Nicolas Cruz-Bournazou

The method is agnostic to the particular nature of the time series and can be adapted for any task, for example, online monitoring, predictive control, design of experiments, etc.

Imputation Irregular Time Series +1

Forecasting Irregularly Sampled Time Series using Graphs

1 code implementation22 May 2023 Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme

Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences.

Astronomy Multivariate Time Series Forecasting +1

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development

no code implementations2 Sep 2022 Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese Schermeyer, Katharina Paulick, Maxim Borisyak, Mariano Nicolas Cruz-Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme, Peter Neubauer, Ernesto Martinez

ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks.

Model Selection Probabilistic Programming

Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping

no code implementations25 Dec 2021 Judit Aizpuru, Annina Karolin Kemmer, Jong Woo Kim, Stefan Born, Peter Neubauer, Mariano N. Cruz Bournazou, Tilman Barz

TheDissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation. Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results.

Dynamic Time Warping

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