Do We Need Deep Learning in Time Series
Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”
- Do We Really Need Deep Learning Models for Time Series Forecasting? by: Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, Hadi Samer Jomaa
- Shereen Elsayed: Research Gate Linkedin
- Daniela Thyssens: Research Gate Linkedin
Daniela Thyssens
Daniela Thyssens is a first-year doctoral student in Data Analytics at the Information Science and Machine Learning Lab, University Hildesheim, Germany, under Prof. Dr. Dr. Schmidt-Thieme. Before joining the research lab, she graduated with a BSc. Hons. in Economics from City, University of London and completed two postgraduate programs: an MSc. Quantitative Economics at the Graduate School of Economics, Finance and Management in Frankfurt, Germany and an MSc. Data Analytics at the University of Hildesheim. Currently, she is working on solving combinatorial optimization problems and specifically routing problems by combining well-known operations research methods with deep learning, while still being very interested in the field of time series forecasting and especially the establishment of well-configured machine learning baselines that ensure a true progression in developing new time series forecasting models.
Shereen Elsayed
Shereen Elsayed is a Ph.D. student in Data Analytics at the Information Science and Machine Learning Lab, University Hildesheim, Germany, under the supervision of Prof. Dr. Dr. Schmidt-Thieme. Before joining the research lab, she graduated with a BSc. in Computer science from Cairo University, Egypt, and completed her MSc. degree in Data Analytics at the University of Hildesheim. Currently, she is working on Fashion-Based Recommender systems and time series analysis.
