1 code implementation • 7 Mar 2024 • Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme
Time series forecasting attempts to predict future events by analyzing past trends and patterns.
Hyperparameter Optimization Probabilistic Time Series Forecasting +1
1 code implementation • 19 Jul 2023 • Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
In the early observation period of a time series, there might be only a few historic observations available to learn a 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
1 code implementation • 5 Dec 2022 • Shayan Jawed, Lars Schmidt-Thieme
We show that following similar intuition from multi-task learning to exploit correlations among forecast horizons, we can model multiple quantile estimates as auxiliary tasks for each of the forecast horizon to improve forecast accuracy across the quantile estimates compared to modeling only a single quantile estimate.
no code implementations • 9 Feb 2022 • Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches, Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research.
no code implementations • 9 Feb 2019 • Shayan Jawed, Eya Boumaiza, Josif Grabocka, Lars Schmidt-Thieme
An active area of research is to increase the safety of self-driving vehicles.