Publications

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

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). 108(7) 1068–1093.
[ 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 = {Elasticity Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting Prediction Self-aware-computing Telescope descartes t_journalmagazine myown}, month = 7, number = 7, pages = {1068 - 1093}, title = {{Time Series Forecasting for Self-Aware Systems}}, volume = 108, 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 %N 7 %P 1068 - 1093 %T Time Series Forecasting for Self-Aware Systems %V 108

Dissertation oder Abschlussarbeit [ nach oben ]

Dynamic Hybrid Forecasting for Self-Aware Systems. Züfle, Marwin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, Oktober).
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 = {Automated_model_learning Chameleon Online_monitoring_and_forecasting Optimization Performance Prediction Self-aware-computing Supervised_by_Nikolas_Herbst Telescope Thesis_supervised_by_Andre_Bauer Thesis_supervised_by_SE_member descartes myown se2_mastersthesis t_studentthesis}, 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 ]

Libra: A Benchmark for Time Series Forecasting Methods. Bauer, André; Züfle, Marwin; Eismann, Simon; Grohmann, Johannes; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{bauer2021benchmark, address = {New York, NY, USA}, author = {Bauer, Andr{é} and Z{ü}fle, Marwin and Eismann, Simon and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {benchmark descartes forecasting metrics_and_benchmarking_methodologies t_full timeseries myown}, month = {April}, organization = {ACM}, title = {Libra: A Benchmark for Time Series Forecasting Methods}, year = 2021 }
%0 Conference Paper %1 bauer2021benchmark %A Bauer, André %A Züfle, Marwin %A Eismann, Simon %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) %C New York, NY, USA %D 2021 %T Libra: A Benchmark for Time Series Forecasting Methods

A Concept for Crowd-sensed Prediction of Mobile Network Connectivity. Herrnleben, Stefan; Zeidler, Bernd; Züfle, Marwin; Krupitzer, Christian; Kounev, Samuel; in GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 (2020).
[ BibTeX ] [ EndNote ]
 
@inproceedings{HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity, author = {Herrnleben, Stefan and Zeidler, Bernd and Z{ü}fle, Marwin and Krupitzer, Christian and Kounev, Samuel}, booktitle = {GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020}, keywords = {Application-aware Automated_model_learning Cloud Internet_of_Things Networking Prediction Self-adaptive-systems descartes myown t_workshop}, month = {February}, title = {{A Concept for Crowd-sensed Prediction of Mobile Network Connectivity}}, year = 2020 }
%0 Conference Paper %1 HeZeZuKrKo-FGMLN2020-Prediction-of-Mobile-Network-Connectivity %A Herrnleben, Stefan %A Zeidler, Bernd %A Züfle, Marwin %A Krupitzer, Christian %A Kounev, Samuel %B GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020 %D 2020 %T A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. Bauer, André; Züfle, Marwin; Grohmann, Johannes; Schmitt, Norbert; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the ACM/SPEC International Conference on Performance Engineering (2020). 48–55.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.
@inproceedings{BaZuGrScHeKo-ICPE20-Seasonal-Forecast, abstract = {Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.}, 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 ACM/SPEC International Conference on Performance Engineering}, keywords = {Online_monitoring_and_forecasting PRISMA Prediction descartes myown t_short}, month = {April}, pages = {48&ndash;55}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '20}, 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 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2020 %I Association for Computing Machinery (ACM) %P 48--55 %T An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series %U https://doi.org/10.1145/3358960.3379123 %X Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the "No-Free-Lunch Theorem" into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.

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; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (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 = {Online_monitoring_and_forecasting Prediction Telescope Tool descartes t_short myown}, 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

A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation. Züfle, Marwin; Kounev, Samuel; in Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications (2020).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{ZuKo-AAIA2020-Framework, author = {Z{ü}fle, Marwin and Kounev, Samuel}, booktitle = {Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications}, keywords = {descartes myown online_monitoring_and_forecasting prediction statistical_estimation_and_machine_learning t_short}, month = {September}, title = {A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation}, year = 2020 }
%0 Conference Paper %1 ZuKo-AAIA2020-Framework %A Züfle, Marwin %A Kounev, Samuel %B Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications %D 2020 %T A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation

To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows. Züfle, Marwin; Krupitzer, Christian; Erhard, Florian; Grohmann, Johannes; Kounev, Samuel; in Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (2020). 19–36.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ 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.}, address = {Cham}, 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 = {Cyber-Physical_Systems Internet_of_Things PRISMA Performance Prediction Reliability Statistical_estimation_and_machine_learning descartes myown t_full}, month = {March}, pages = {19&ndash;36}, publisher = {Springer}, 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 %C Cham %D 2020 %I Springer %P 19--36 %T To Fail Or Not To Fail: Predicting Hard Disk Drive Failure Time Windows %U https://doi.org/10.1007/978-3-030-43024-5_2 %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.

Autonomic Forecasting Method Selection: Examination and Ways Ahead. Züfle, Marwin; Bauer, André; Lesch, Veronika; Krupitzer, Christian; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (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 = {Internet_of_Things Online_monitoring_and_forecasting PRISMA Performance Prediction Statistical_estimation_and_machine_learning descartes t_full myown}, 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; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (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 = {Telescope Online_monitoring_and_forecasting myown t_tutorial descartes}, 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

Utilizing Clustering to Optimize Resource Demand Estimation Approaches. Grohmann, Johannes; Eismann, Simon; Bauer, Andre; Zuefle, Marwin; Herbst, Nikolas; Kounev, Samuel; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134–139.
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ 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 = {GoogleResearchAward Instrumentation_profiling_and_workload_characterization LibReDE Optimization PRISMA Performance Self-adaptive-systems Self-aware-computing Statistical_estimation_and_machine_learning descartes t_workshop myown}, month = {June}, pages = {134&ndash;139}, publisher = {IEEE}, 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 %I IEEE %P 134--139 %T Utilizing Clustering to Optimize Resource Demand Estimation Approaches %U https://doi.org/10.1109/FAS-W.2019.00043 %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; in Proceedings of the International work-conference on Time Series (ITISE 2017) (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 = {Online_monitoring_and_forecasting Optimization Prediction Statistical_estimation_and_machine_learning Telescope Tool descartes t_extendedabstract myown}, 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.

Kapitel (Teil eines Buches) [ nach oben ]

Towards a Self-Aware Prediction of Critical States. Züfle, Marwin; in Organic Computing: Doctoral Dissertation Colloquium 2020, S. Tomforde, C. Krupitzer (Hrsg.) (2020).
(to appear)
[ BibTeX ] [ EndNote ]
 
@incollection{zufle2020towards, author = {Z{ü}fle, Marwin}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {Self-aware-computing descartes t_bookchapter myown statistical_estimation_and_machine_learning prediction online_monitoring_and_forecasting}, month = {jul}, note = {(to appear)}, title = {Towards a Self-Aware Prediction of Critical States}, year = 2020 }
%0 Book Section %1 zufle2020towards %A Züfle, Marwin %B Organic Computing: Doctoral Dissertation Colloquium 2020 %D 2020 %E Tomforde, Sven %E Krupitzer, Christian %T Towards a Self-Aware Prediction of Critical States

Report [ nach oben ]

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 = {Industry_4_0 Survey descartes t_techreport myown}, 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

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 = {Cyber-Physical_Systems Industry_4_0 Internet_of_Things Online_monitoring_and_forecasting Prediction Reliability Survey descartes t_techreport myown}, 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

Sonstiges [ nach oben ]

Artificial Intelligence in Medicine - From Data Collection to Prediction. Züfle, Marwin; Leppich, Robert; (2019, November).
[ BibTeX ] [ EndNote ]
 
@misc{talk-ZuLe, author = {Z{ü}fle, Marwin and Leppich, Robert}, howpublished = {Invited Talk at Klinikum N{ü}rnberg}, keywords = {Cloud Health IoT Prediction Sports Statistical_estimation_and_machine_learning Talks descartes myown t_talk}, 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

Predictive Maintenance for Industry 4.0. Züfle, Marwin; (2018, April).
[ BibTeX ] [ EndNote ] [ Download ]
 
@misc{talk-Zuefle-SelPhyS, author = {Z{ü}fle, Marwin}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2018}, keywords = {Prediction Statistical_estimation_and_machine_learning Talks Telescope descartes se2_talk t_talk myown}, 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