4 citations · 6 across the 6 of their papers we have counts for
6 papers
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme
Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep le…
Forecasting Early with Meta Learning
Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi +1
In the early observation period of a time series, there might be only a few historic observations available to learn a model. However, in cases where an existing prior set of datas…
Forecasting Irregularly Sampled Time Series using Graphs
Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz +5
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of…
Auxiliary Quantile Forecasting with Linear Networks
Shayan Jawed, Lars Schmidt-Thieme
We propose a novel multi-task method for quantile forecasting with shared Linear layers. Our method is based on the Implicit quantile learning approach, where samples from the Unif…
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed +8
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financia…
Data-Driven Vehicle Trajectory Forecasting
Shayan Jawed, Eya Boumaiza, Josif Grabocka +1
An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future tr…