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Marwin Züfle
Marwin Züfle
Doctoral Researcher, Chair of Software Engineering, University of Wuerzburg
Verified email at uni-wuerzburg.de - Homepage
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Cited by
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Year
A survey on predictive maintenance for industry 4.0
C Krupitzer, T Wagenhals, M Züfle, V Lesch, D Schäfer, A Mozaffarin, ...
arXiv preprint arXiv:2002.08224, 2020
462020
A survey on human machine interaction in industry 4.0
C Krupitzer, S Müller, V Lesch, M Züfle, J Edinger, A Lemken, D Schäfer, ...
arXiv preprint arXiv:2002.01025, 2020
462020
Autonomic forecasting method selection: Examination and ways ahead
M Züfle, A Bauer, V Lesch, C Krupitzer, N Herbst, S Kounev, V Curtef
2019 IEEE International conference on autonomic computing (ICAC), 167-176, 2019
302019
Time series forecasting for self-aware systems
A Bauer, M Züfle, N Herbst, A Zehe, A Hotho, S Kounev
Proceedings of the IEEE 108 (7), 1068-1093, 2020
292020
A machine learning-based workflow for automatic detection of anomalies in machine tools
M Züfle, F Moog, V Lesch, C Krupitzer, S Kounev
ISA transactions 125, 445-458, 2022
272022
A literature review of IoT and CPS—What they are, and what they are not
V Lesch, M Züfle, A Bauer, L Iffländer, C Krupitzer, S Kounev
Journal of Systems and Software 200, 111631, 2023
212023
Telescope: An automatic feature extraction and transformation approach for time series forecasting on a level-playing field
A Bauer, M Züfle, N Herbst, S Kounev, V Curtef
2020 IEEE 36th International Conference on Data Engineering (ICDE), 1902-1905, 2020
212020
An automated forecasting framework based on method recommendation for seasonal time series
A Bauer, M Züfle, J Grohmann, N Schmitt, N Herbst, S Kounev
Proceedings of the ACM/SPEC International Conference on Performance …, 2020
212020
Telescope: a hybrid forecast method for univariate time series
M Züfle, A Bauer, N Herbst, V Curtef, S Kounev
International work-conference on Time Series (ITISE 2017), 2017
192017
Libra: A benchmark for time series forecasting methods
A Bauer, M Züfle, S Eismann, J Grohmann, N Herbst, S Kounev
Proceedings of the ACM/SPEC International Conference on Performance …, 2021
152021
To fail or not to fail: Predicting hard disk drive failure time windows
M Züfle, C Krupitzer, F Erhard, J Grohmann, S Kounev
Measurement, Modelling and Evaluation of Computing Systems: 20th …, 2020
132020
A predictive maintenance methodology: predicting the time-to-failure of machines in industry 4.0
M Züfle, J Agne, J Grohmann, I Dörtoluk, S Kounev
2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 1-8, 2021
102021
A survey on predictive maintenance for industry 4.0. arXiv 2020
C Krupitzer, T Wagenhals, M Züfle, V Lesch, D Schäfer, A Mozaffarin, ...
arXiv preprint arXiv:2002.08224, 0
9
A Survey on Human Machine Interaction in Industry 4.0. arXiv 2020
C Krupitzer, S Müller, V Lesch, M Züfle, J Edinger, A Lemken, D Schäfer, ...
arXiv preprint arXiv:2002.01025, 0
7
Utilizing clustering to optimize resource demand estimation approaches
J Grohmann, S Eismann, A Bauer, M Zuefle, N Herbst, S Kounev
2019 IEEE 4th International Workshops on Foundations and Applications of …, 2019
62019
A framework for time series preprocessing and history-based forecasting method recommendation
M Züfle, S Kounev
2020 15th Conference on Computer Science and Information Systems (FedCSIS …, 2020
52020
Machine Learning Model Update Strategies for Hard Disk Drive Failure Prediction
M Züfle, F Erhard, S Kounev
2021 20th IEEE International Conference on Machine Learning and Applications …, 2021
42021
Best practices for time series forecasting (tutorial)
A Bauer, M Züfle, N Herbst, S Kounev
2019 IEEE 4th International Workshops on Foundations and Applications of …, 2019
32019
Dynamic Hybrid Forecasting for Self-Aware Systems
M Züfle
Master Thesis. Würzburg, Germany: Department of Computer Science …, 2017
32017
Recommendations for data-driven degradation estimation with case studies from manufacturing and dry-bulk shipping
N Finke, M Mohr, A Lontke, M Züfle, S Kounev, R Möller
International Conference on Research Challenges in Information Science, 189-204, 2021
22021
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