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Florian Grimm

Florian Grimm, M.Sc.Research team Digitization and Management

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Building 5
Room 011
Tel. +49 7121 271 1498
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Areas of Responsibilty

Research assistant in the research team Digitization and Management

Research Topics
  • Artificial Intelligence
    • Machine Learning
    • Deep Learning
  • Data Analysis
    • Time Series Analysis/Prediction/Classification
    • Data Augmentation
Current Research Projects

ANIMATE

Vita
  • Mater of Science in Media Informatics (MSc), Eberhard Karls University of Tübingen, Tübingen
    • Study abroad semester at the Università degli Studi Roma Tre, Rome (Italy)
    • Internship in the field of data analysis, Daimler AG, Untertürkheim, Germany
    • Master's thesis undertaken at Daimler AG: ‘Classifying Industrial Welding Data Using Support Vector Machines and Neural Networks’
  • Bachelor of Science in Media Informatics (BSc), Eberhard Karls Universität Tübingen, Tübingen
  • Apprenticeship as an IT specialist in the field of system integration, Deutsche Telekom AG, Regensburg
Publications
  • D. Kiefer, M. Bauer and F. Grimm: Univariate Time Series Forecasting: Machine Learning Prediction of the Best Suitable Forecast Model based on Time Series Characteristics. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • M. Bauer, D. Kiefer and F. Grimm: Sales Forecasting under Economic Crisis: A Case study of the Impact of the COVID19 Crisis to the predictability of Sales of a Medium-Sized Enterprise. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • F. Grimm, D. Kiefer and M. Bauer: Univariate Time Series Forecasting by Investigating Intermittence and Demand Individually. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • D. Kiefer, C. van Dinther and J. Spitzmüller: Digital Innovation Culture – A Systematic Literature Review. In: Proceeding of the 16. Internationale Tagung der Wirtschaftsinformatik (WI). 16. Auflage. Duisburg-Essen 2021.
  • D. Kiefer, F. Grimm, M. Bauer and C. van Dinther, “Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods,” in Proc. 54rd Hawaii Int. Conf. Syst. Sci., Maui, HI, USA, Jan. 2021.