1 code implementation • 7 Apr 2022 • Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, Lars Schmidt-Thieme
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
1 code implementation • 13 Oct 2021 • Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme
Time series data is ubiquitous in research as well as in a wide variety of industrial applications.
1 code implementation • 24 Aug 2022 • Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme
Because of the asynchronous nature, they pose a significant challenge to deep learning architectures, which presume that the time series presented to them are regularly sampled, fully observed, and aligned with respect to time.
1 code implementation • 5 Oct 2022 • Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme
Results indicate an improvement in negative loglikelihood error by up to 32% on real-world datasets and 85% on synthetic datasets when using the Tripletformer compared to the next best model.
1 code implementation • 22 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.
Ranked #1 on Multivariate Time Series Forecasting on USHCN-Daily
no code implementations • 30 Nov 2023 • Thorben Werner, Johannes Burchert, Lars Schmidt-Thieme
Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data annotation costs.
no code implementations • 10 Apr 2024 • Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi, Diego Coello de Portugal, Maximilian Stubbemann, Lars Schmidt-Thieme
For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification.