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Florian Grimm M.Sc.
Research team Digitization and Management
+49 7121 271 1498
florian.grimm@reutlingen-university.de
Building 5, Room 011
Research assistant in the research team Digitization and Management
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Data Analysis
- Time Series Analysis/Prediction/Classification
- Data Augmentation
ANIMATE
- 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
Grimm, Florian
2024 | |
| Kiefer, Daniel; Wezel, Stefan; Böttcher, Alexander; Grimm, Florian; Straub, Tim; Bitsch, Günter; van Dinther, Clemens (2024): Anomaly detection in hobbing tool images: using an unsupervised deep learning approach in manufacturing industry. - In: Procedia computer science 232 (5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)). - S. 2396-2405. - DOI: https://doi.org/10.1016/j.procs.2024.02.058 | BibTeX | RIS DOIURN |
| Grimm, Florian; Kiefer, Daniel; Straub, Tim; Bitsch, Günter; van Dinther, Clemens (2024): Automatic gear tooth alignment in vision based preventive maintenance. - In: Procedia computer science 232 (5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)). - S. 1564-1572. - DOI: https://doi.org/10.1016/j.procs.2024.01.154 | BibTeX | RIS DOIURN |
| Höllig, Jacqueline; Thoma, Steffen; Grimm, Florian (2024): XTSC-bench: quantitative benchmarking for explainers on time series classification. - In: 22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023) : 15-17 December 2023, Jacksonville, Florida, proceedings. - Piscataway : IEEE. - S. 1126-1131. - DOI: https://doi.org/10.1109/ICMLA58977.2023.00168 | BibTeX | RIS DOI |
2022 | |
| Kiefer, Daniel; Grimm, Florian; van Dinther, Clemens (2022): Artificial intelligence in supply chain management: investigation of transfer learning to improve demand forecasting of intermittent time series with deep learning. - In: Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022), 4-7 January 2022, virtual event/Maui. - Honolulu : University of Hawai'i at Manoa. - S. 1656-1665. - ISBN: 978-0-9981331-5-7. - URL: http://hdl.handle.net/10125/79537 | BibTeX | RIS URN |
2021 | |
| Kiefer, Daniel; Grimm, Florian; Bauer, Markus; van Dinther, Clemens (2021): Demand forecasting intermittent and lumpy time series: comparing statistical, machine learning and deep learning methods. - In: Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54), 4-8 January 2021, online. - Honolulu : University of Hawai'i at Manoa. - S. 1425-1434. - ISBN: 978-0-9981331-4-0. - DOI: https://doi.org/10.24251/HICSS.2021.172 | BibTeX | RIS DOIURN |
| Bauer, Markus; Kiefer, Daniel; Grimm, Florian (2021): 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: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 163-172. - ISBN: 978-981-16-3264-8. - DOI: https://doi.org/10.1007/978-981-16-3264-8_16 | BibTeX | RIS DOI |
| Grimm, Florian; Kiefer, Daniel; Bauer, Markus (2021): Univariate time series forecasting by investigating intermittence and demand individually. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 143-151. - ISBN: 978-981-16-3266-2. - DOI: https://doi.org/10.1007/978-981-16-3264-8_14 | BibTeX | RIS DOI |
| Kiefer, Daniel; Bauer, Markus; Grimm, Florian (2021): Univariate time series forecasting: machine learning prediction of the best suitable forecast model based on time series characteristics. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 152-162. - ISBN: 978-981-16-3266-2. - DOI: https://doi.org/10.1007/978-981-16-3264-8_15 | BibTeX | RIS DOI |
