Publications

Artikel in einem Journal, einer Zeitung oder einem Magazin [ nach oben ]

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  Time Series Forecasting for Self-Aware Systems. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Zehe, Albin; Hotho, Andreas; Kounev, Samuel; in Proceedings of the IEEE (2020).

  To appear.
  [ BibTeX ] [ EndNote ]
   
  @article{BaZuHeZeHoKo-PIEEE-Forecasting, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Zehe, Albin and Hotho, Andreas and Kounev, Samuel}, journal = {Proceedings of the IEEE}, keywords = {descartes myown Online_monitoring_and_forecasting Self-aware-computing Telescope Elasticity Prediction Metrics_and_benchmarking_methodologies}, note = {To appear.}, title = {{Time Series Forecasting for Self-Aware Systems}}, year = 2020 }
  %0 Journal Article %1 BaZuHeZeHoKo-PIEEE-Forecasting %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Zehe, Albin %A Hotho, Andreas %A Kounev, Samuel %D 2020 %J Proceedings of the IEEE %T Time Series Forecasting for Self-Aware Systems

Dissertation oder Abschlussarbeit [ nach oben ]

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  Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press. Agne, Joachim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Agne2020, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Agne, Joachim}, keywords = {Industry_4_0 Thesis_supervised_by_Marwin_Zuefle Online_monitoring_and_forecasting descartes feature_engineering Thesis_supervised_by_SE_member Cyber-Physical_Systems se2_mastersthesis}, month = {February}, school = {University of W{ü}rzburg}, title = {{Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press}}, type = {{MasterThesis}}, year = 2020 }
  %0 Thesis %1 Agne2020 %A Agne, Joachim %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press
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  Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation. Erhard, Florian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Erhard2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Erhard, Florian}, keywords = {Thesis_supervised_by_Marwin_Zuefle feature_engineering Thesis_supervised_by_Christian_Krupitzer prediction descartes Thesis_supervised_by_SE_member statistical_estimation_and_machine_learning se2_mastersthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {{Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Erhard2019 %A Erhard, Florian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation
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  An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics. Dreher, Tim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Dreher2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Dreher, Tim}, keywords = {Thesis_supervised_by_Marwin_Zuefle descartes feature_engineering Thesis_supervised_by_SE_member Thesis_supervised_by_Christian_Krupitzer thesis_supervised_by_se_member prediction se2_mastersthesis}, month = {October}, school = {University of W{ü}rzburg}, title = {{An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics}}, type = {{Bachelor Thesis}}, year = 2019 }
  %0 Thesis %1 Dreher2019 %A Dreher, Tim %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics
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  Design of an Industry 4.0 Demonstrator. König, Philipp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{Koenig2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {K{ö}nig, Philipp}, keywords = {Thesis_supervised_by_Veronika_Lesch Thesis_supervised_by_Marwin_Zuefle descartes Thesis_supervised_by_SE_member se2_mastersthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {{Design of an Industry 4.0 Demonstrator}}, type = {{Bachelor Thesis}}, year = 2018 }
  %0 Thesis %1 Koenig2018 %A König, Philipp %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Design of an Industry 4.0 Demonstrator
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  Time Series Forecasting: A Recommendation for Method Selection. Ort, Jannik; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{BA-Ort-2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Ort, Jannik}, keywords = {Thesis_supervised_by_Marwin_Zuefle Thesis_supervised_by_Andre_Bauer Prediction descartes Thesis_supervised_by_SE_member Statistical_estimation_and_machine_learning se2_mastersthesis}, month = {September}, school = {University of W{ü}rzburg}, title = {{Time Series Forecasting: A Recommendation for Method Selection}}, type = {{Bachelor Thesis}}, year = 2018 }
  %0 Thesis %1 BA-Ort-2018 %A Ort, Jannik %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Time Series Forecasting: A Recommendation for Method Selection
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  Comparing Machine Learning Approaches for Multivariate Time Series Forecasting. Otto, Dominik; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{OttoMultiTelescope2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Otto, Dominik}, keywords = {Thesis_supervised_by_Marwin_Zuefle Performance Telescope Optimization Thesis_supervised_by_Andre_Bauer Prediction descartes Thesis_supervised_by_SE_member Statistical_estimation_and_machine_learning se2_mastersthesis}, month = {April}, school = {University of W{ü}rzburg}, title = {{Comparing Machine Learning Approaches for Multivariate Time Series Forecasting}}, type = {{Bachelor Thesis}}, year = 2018 }
  %0 Thesis %1 OttoMultiTelescope2018 %A Otto, Dominik %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Comparing Machine Learning Approaches for Multivariate Time Series Forecasting
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  Automatisiertes Forecasting mit Feature Engineering. Augustin, Britta; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018).
  [ BibTeX ] [ EndNote ]
   
  @mastersthesis{BrittaAFFE2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Augustin, Britta}, keywords = {Thesis_supervised_by_Marwin_Zuefle Automated_model_learning Thesis_supervised_by_Andre_Bauer descartes Thesis_supervised_by_SE_member Telescope se2_mastersthesis}, month = {April}, school = {University of W{ü}rzburg}, title = {{Automatisiertes Forecasting mit Feature Engineering}}, type = {{Bachelor Thesis}}, year = 2018 }
  %0 Thesis %1 BrittaAFFE2018 %A Augustin, Britta %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Automatisiertes Forecasting mit Feature Engineering
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  Dynamic Hybrid Forecasting for Self-Aware Systems. Züfle, Marwin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017).

  Würzburg Software Engineering Award sponsored by infosim
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @mastersthesis{Zuefle2017Dynamic, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Z{ü}fle, Marwin}, keywords = {descartes Self-aware-computing Automated_model_learning Supervised_by_Nikolas_Herbst Performance se2_mastersthesis Thesis_supervised_by_SE_member Online_monitoring_and_forecasting Thesis_supervised_by_Andre_Bauer Optimization Telescope myown Prediction Chameleon}, month = {October}, note = {<b>W{ü}rzburg Software Engineering Award sponsored by infosim</b>}, school = {University of W{ü}rzburg}, title = {{Dynamic Hybrid Forecasting for Self-Aware Systems}}, type = {{Master Thesis}}, year = 2017 }
  %0 Thesis %1 Zuefle2017Dynamic %A Züfle, Marwin %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2017 %T Dynamic Hybrid Forecasting for Self-Aware Systems

Artikel in Konferenzband [ nach oben ]

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  An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. Bauer, André; Züfle, Marwin; Grohmann, Johannes; Schmitt, Norbert; Herbst, Nikolas; Kounev, Samuel; (2020).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @inproceedings{BaZuGrScHeKo-ICPE20-Seasonal-Forecast, address = {New York, NY, USA}, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Grohmann, Johannes and Schmitt, Norbert and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020)}, keywords = {descartes Online_monitoring_and_forecasting PRISMA myown Prediction publication_type_shortpaper}, month = {April}, publisher = {ACM}, title = {{An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series}}, year = 2020 }
  %0 Conference Paper %1 BaZuGrScHeKo-ICPE20-Seasonal-Forecast %A Bauer, André %A Züfle, Marwin %A Grohmann, Johannes %A Schmitt, Norbert %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE 2020) %C New York, NY, USA %D 2020 %I ACM %T An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series %U https://doi.org/10.1145/3358960.3379123
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  To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows. Züfle, Marwin; Krupitzer, Christian; Erhard, Florian; Grohmann, Johannes; Kounev, Samuel; in MMB 2020 (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.
  @inproceedings{ZuKrErGrKo-MMB2020-ToFailOrNotToFail, abstract = {Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.}, author = {Z{ü}fle, Marwin and Krupitzer, Christian and Erhard, Florian and Grohmann, Johannes and Kounev, Samuel}, booktitle = {Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems}, keywords = {descartes Cyber-Physical_Systems PRISMA Reliability Internet_of_Things Statistical_estimation_and_machine_learning Performance myown Prediction}, month = {March}, series = {MMB 2020}, title = {{To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows}}, year = 2020 }
  %0 Conference Paper %1 ZuKrErGrKo-MMB2020-ToFailOrNotToFail %A Züfle, Marwin %A Krupitzer, Christian %A Erhard, Florian %A Grohmann, Johannes %A Kounev, Samuel %B Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems %D 2020 %T To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows %X Due to the increasing size of today's data centers as well as the expectation of 24/7 availability, the complexity in the administration of hardware continuously icreases. Techniques as the Self-Monitoring, Analysis, and Reporting Technology (S.M.A.R.T.) support the monitoring of the hardware. However, those techniques often lack algorithms for intelligent data analytics. Especially, the integration of machine learning to identify potential failures in advance seems to be promising to reduce administration overhead. In this work, we present three machine learning approaches to (i) identify imminent failures, (ii) predict time windows for failures, as well as (iii) predict the exact time-to-failure. In a case study with real data from 369 hard disks, we achieve an F1-score of up to 98.0% and 97.6% for predicting potential failures with two or multiple time windows, respectively, and a hit rate of 84.9% (with a mean absolute error of 4.5 hours) for predicting the time-to-failure.
• 
  Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; (2020).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{BaZuHeKoCu-ICDE-Telescope, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 36th International Conference on Data Engineering (ICDE)}, keywords = {descartes Tool Online_monitoring_and_forecasting Telescope myown Prediction}, month = {April}, title = {Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field}, year = 2020 }
  %0 Conference Paper %1 BaZuHeKoCu-ICDE-Telescope %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 36th International Conference on Data Engineering (ICDE) %D 2020 %T Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field
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  Autonomic Forecasting Method Selection: Examination and Ways Ahead. Züfle, Marwin; Bauer, André; Lesch, Veronika; Krupitzer, Christian; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{ZuBaLeKrHeKoCu2019-ICAC-Autonomic, author = {Z{ü}fle, Marwin and Bauer, Andr{é} and Lesch, Veronika and Krupitzer, Christian and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 16th {IEEE} International Conference on Autonomic Computing (ICAC)}, keywords = {descartes Online_monitoring_and_forecasting Statistical_estimation_and_machine_learning PRISMA myown Prediction Internet_of_Things Performance}, month = {June}, publisher = {IEEE}, title = {{Autonomic Forecasting Method Selection: Examination and Ways Ahead}}, year = 2019 }
  %0 Conference Paper %1 ZuBaLeKrHeKoCu2019-ICAC-Autonomic %A Züfle, Marwin %A Bauer, André %A Lesch, Veronika %A Krupitzer, Christian %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) %D 2019 %I IEEE %T Autonomic Forecasting Method Selection: Examination and Ways Ahead
• 
  Best Practices for Time Series Forecasting. Bauer, André; Züfle, Marwin; Herbst, Nikolas; Kounev, Samuel; (2019).
  [ BibTeX ] [ EndNote ] [ Download ]
   
  @inproceedings{BaZuHeKo2019-ICAC-ForecastingTutorial, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {{2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)}}, keywords = {descartes Online_monitoring_and_forecasting Telescope myown publication_type_tutorial}, month = {June}, title = {{Best Practices for Time Series Forecasting}}, year = 2019 }
  %0 Conference Paper %1 BaZuHeKo2019-ICAC-ForecastingTutorial %A Bauer, André %A Züfle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) %D 2019 %T Best Practices for Time Series Forecasting
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  Utilizing Clustering to Optimize Resource Demand Estimation Approaches. Grohmann, Johannes; Eismann, Simon; Bauer, Andre; Zuefle, Marwin; Herbst, Nikolas; Kounev, Samuel; (2019). 134-139.
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.
  @inproceedings{GrEiBaZuHeKo2019-ICAC-RDEClustering, abstract = {Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.}, author = {Grohmann, Johannes and Eismann, Simon and Bauer, Andre and Zuefle, Marwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)}, keywords = {descartes myown Self-aware-computing PRISMA GoogleResearchAward Performance Statistical_estimation_and_machine_learning LibReDE Optimization Instrumentation_profiling_and_workload_characterization Self-adaptive-systems}, month = {June}, pages = {134-139}, title = {{Utilizing Clustering to Optimize Resource Demand Estimation Approaches}}, year = 2019 }
  %0 Conference Paper %1 GrEiBaZuHeKo2019-ICAC-RDEClustering %A Grohmann, Johannes %A Eismann, Simon %A Bauer, Andre %A Zuefle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) %D 2019 %P 134-139 %T Utilizing Clustering to Optimize Resource Demand Estimation Approaches %X Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.
• 
  Telescope: A Hybrid Forecast Method for Univariate Time Series. Züfle, Marwin; Bauer, André; Herbst, Nikolas; Curtef, Valentin; Kounev, Samuel; (2017).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
   
  Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.
  @inproceedings{ZBBHCK2017-ITISE-Telescope, abstract = {Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.}, author = {Z{ü}fle, Marwin and Bauer, Andr{é} and Herbst, Nikolas and Curtef, Valentin and Kounev, Samuel}, booktitle = {{Proceedings of the International work-conference on Time Series (ITISE 2017)}}, keywords = {descartes Tool Statistical_estimation_and_machine_learning Online_monitoring_and_forecasting Optimization publication_type_extendedabstract Telescope myown Prediction}, month = {September}, title = {{Telescope: A Hybrid Forecast Method for Univariate Time Series}}, year = 2017 }
  %0 Conference Paper %1 ZBBHCK2017-ITISE-Telescope %A Züfle, Marwin %A Bauer, André %A Herbst, Nikolas %A Curtef, Valentin %A Kounev, Samuel %B Proceedings of the International work-conference on Time Series (ITISE 2017) %D 2017 %T Telescope: A Hybrid Forecast Method for Univariate Time Series %X Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.

Report [ nach oben ]

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  A Survey on Predictive Maintenance for Industry 4.0 Krupitzer, Christian; Wagenhals, Tim; Züfle, Marwin; Lesch, Veronika; Schäfer, Dominik; Mozaffarin, Amin; Edinger, Janick; Becker, Christian; Kounev, Samuel; (2020).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @techreport{Krupitzer2020ASurveyPdM, author = {Krupitzer, Christian and Wagenhals, Tim and Z{ü}fle, Marwin and Lesch, Veronika and Sch{ä}fer, Dominik and Mozaffarin, Amin and Edinger, Janick and Becker, Christian and Kounev, Samuel}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and Syntax Systems GmbH and MOZYS Engineering GmbH}, journal = {arXiv}, keywords = {descartes Cyber-Physical_Systems Reliability Internet_of_Things Online_monitoring_and_forecasting Industry_4_0 myown Survey Prediction}, month = {Feb}, title = {{A Survey on Predictive Maintenance for Industry 4.0}}, year = 2020 }
  %0 Report %1 Krupitzer2020ASurveyPdM %A Krupitzer, Christian %A Wagenhals, Tim %A Züfle, Marwin %A Lesch, Veronika %A Schäfer, Dominik %A Mozaffarin, Amin %A Edinger, Janick %A Becker, Christian %A Kounev, Samuel %D 2020 %J arXiv %T A Survey on Predictive Maintenance for Industry 4.0 %U https://arxiv.org/abs/2002.08224
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  A Survey on Human Machine Interaction in Industry 4.0 Krupitzer, Christian; Müller, Sebastian; Lesch, Veronika; Züfle, Marwin; Edinger, Janick; Lemken, Alexander; Schäfer, Dominik; Kounev, Samuel; Becker, Christian; (2020).
  [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
   
  @techreport{Krupitzer2020ASurvey, author = {Krupitzer, Christian and M{ü}ller, Sebastian and Lesch, Veronika and Z{ü}fle, Marwin and Edinger, Janick and Lemken, Alexander and Sch{ä}fer, Dominik and Kounev, Samuel and Becker, Christian}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and ioxp GmbH and Syntax Systems GmbH}, journal = {arXiv}, keywords = {descartes myown Industry_4_0 Survey}, month = {Feb}, title = {{A Survey on Human Machine Interaction in Industry 4.0}}, year = 2020 }
  %0 Report %1 Krupitzer2020ASurvey %A Krupitzer, Christian %A Müller, Sebastian %A Lesch, Veronika %A Züfle, Marwin %A Edinger, Janick %A Lemken, Alexander %A Schäfer, Dominik %A Kounev, Samuel %A Becker, Christian %D 2020 %J arXiv %T A Survey on Human Machine Interaction in Industry 4.0 %U https://arxiv.org/abs/2002.01025

Sonstiges [ nach oben ]

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  A Concept for Crowd-sensed Prediction of Mobile Network Connectivity. Herrnleben, Stefan; Zeidler, Bernd; Züfle, Marwin; Krupitzer, Christian; Kounev, Samuel; (2020).
  [ BibTeX ] [ EndNote ]
   
  @misc{HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity, author = {Herrnleben, Stefan and Zeidler, Bernd and Z{ü}fle, Marwin and Krupitzer, Christian and Kounev, Samuel}, howpublished = {GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020}, keywords = {descartes Networking Application-aware Automated_model_learning Internet_of_Things Cloud myown Prediction Self-adaptive-systems}, month = {February}, title = {{A Concept for Crowd-sensed Prediction of Mobile Network Connectivity}}, year = 2020 }
  %0 Generic %1 HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity %A Herrnleben, Stefan %A Zeidler, Bernd %A Züfle, Marwin %A Krupitzer, Christian %A Kounev, Samuel %D 2020 %T A Concept for Crowd-sensed Prediction of Mobile Network Connectivity
• 
  Artificial Intelligence in Medicine - From Data Collection to Prediction. Züfle, Marwin; Leppich, Robert; (2019).
  [ BibTeX ] [ EndNote ]
   
  @misc{talk-ZuLe, author = {Z{ü}fle, Marwin and Leppich, Robert}, howpublished = {Invited Talk at Klinikum N{ü}rnberg}, keywords = {se2_talk descartes Health Internet_of_Things Sports Statistical_estimation_and_machine_learning Cloud myown Prediction Talks}, month = {November}, title = {{Artificial Intelligence in Medicine - From Data Collection to Prediction}}, year = 2019 }
  %0 Generic %1 talk-ZuLe %A Züfle, Marwin %A Leppich, Robert %D 2019 %T Artificial Intelligence in Medicine - From Data Collection to Prediction
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  Predictive Maintenance for Industry 4.0. Züfle, Marwin; (2018).
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  @misc{talk-Zuefle-SelPhyS, author = {Z{ü}fle, Marwin}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2018}, keywords = {se2_talk descartes Telescope myown Prediction Statistical_estimation_and_machine_learning Talks}, month = {April}, title = {{Predictive Maintenance for Industry 4.0}}, year = 2018 }
  %0 Generic %1 talk-Zuefle-SelPhyS %A Züfle, Marwin %D 2018 %T Predictive Maintenance for Industry 4.0