Publications

Journal and Magazine Articles

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis. Stenger, Michael; Bauer, André; Prantl, Thomas; Leppich, Robert; Hudson, Nathaniel; Chard, Kyle; Foster, Ian; Kounev, Samuel; in J. Data and Information Quality (2024). 16(2) Association for Computing Machinery, New York, NY, USA.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, time series can be generated synthetically. For example, electrocardiograms can be synthesized to make them available for building models to predict conditions such as cardiac arrhythmia without leaking patient information. Although many different approaches to time series synthesis have been proposed, evaluating the quality of synthetic time series data poses unique challenges and remains an open problem, as there is a lack of a clear definition of what constitutes a “good” synthesis. To this end, we present a comprehensive literature survey to identify different aspects of synthesis quality and their relationships. Based on this, we propose a definition of synthesis quality and a systematic evaluation procedure for assessing it. With this work, we aim to provide a common language and criteria for evaluating synthetic time series data. Our goal is to promote more rigorous and reproducible research in time series synthesis by enabling researchers and practitioners to generate high-quality synthetic time series data.

@article{10.1145/3666006, abstract = {Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, time series can be generated synthetically. For example, electrocardiograms can be synthesized to make them available for building models to predict conditions such as cardiac arrhythmia without leaking patient information. Although many different approaches to time series synthesis have been proposed, evaluating the quality of synthetic time series data poses unique challenges and remains an open problem, as there is a lack of a clear definition of what constitutes a “good” synthesis. To this end, we present a comprehensive literature survey to identify different aspects of synthesis quality and their relationships. Based on this, we propose a definition of synthesis quality and a systematic evaluation procedure for assessing it. With this work, we aim to provide a common language and criteria for evaluating synthetic time series data. Our goal is to promote more rigorous and reproducible research in time series synthesis by enabling researchers and practitioners to generate high-quality synthetic time series data.}, address = {New York, NY, USA}, author = {Stenger, Michael and Bauer, Andr\'{e} and Prantl, Thomas and Leppich, Robert and Hudson, Nathaniel and Chard, Kyle and Foster, Ian and Kounev, Samuel}, journal = {J. Data and Information Quality}, keywords = {t_journalmagazine}, month = {06}, number = 2, publisher = {Association for Computing Machinery}, title = {Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis}, volume = 16, year = 2024 }
Evaluation is key: a survey on evaluation measures for synthetic time series. Stenger, Michael; Leppich, Robert; Foster, Ian; Kounev, Samuel; Bauer, André; in Journal of Big Data (2024). 11 66.
- [ BibTeX ]
@article{Stenger2024, author = {Stenger, Michael and Leppich, Robert and Foster, Ian and Kounev, Samuel and Bauer, Andr{\'e}}, journal = {Journal of Big Data}, keywords = {t_journalmagazine}, month = {05}, pages = 66, title = {Evaluation is key: a survey on evaluation measures for synthetic time series}, volume = 11, year = 2024 }
Comprehensive Exploration of Synthetic Data Generation: A Survey. Bauer, André; and Trapp, Simon; and Stenger, Michael; and Leppich, Robert; and Kounev, Samuel; and Leznik, Mark; and Chard, Kyle; and Foster, Ian; in arXiv preprint (2024). 103.

https://doi.org/10.48550/arXiv.2401.02524
- [ BibTeX ]
@article{bauer2024comprehensive, author = {Bauer, André and and Trapp, Simon and and Stenger, Michael and and Leppich, Robert and and Kounev, Samuel and and Leznik, Mark and and Chard, Kyle and and Foster, Ian}, journal = {arXiv preprint}, keywords = {t_journalmagazine}, note = {https://doi.org/10.48550/arXiv.2401.02524}, pages = 103, title = {Comprehensive Exploration of Synthetic Data Generation: A Survey}, year = 2024 }
Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field. Bauer, André; Leznik, Mark; Stenger, Michael; Leppich, Robert; Herbst, Nikolas; Kounev, Samuel; Foster, Ian; (2023).
- [ BibTeX ]
@misc{bauer2023telescope, author = {Bauer, André and Leznik, Mark and Stenger, Michael and Leppich, Robert and Herbst, Nikolas and Kounev, Samuel and Foster, Ian}, keywords = {t_journalmagazine}, title = {Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field}, year = 2023 }

Master Thesis

A Processing Framework for Graph-based Urban Area Delineation and Analysis. Gruber, Lorenz; (2024, August). University of Wuerzburg.
- [ BibTeX ]
@mastersthesis{fishnet2024, author = {Gruber, Lorenz}, keywords = {t_interdisciplinary}, month = {08}, school = {University of Wuerzburg}, title = {A Processing Framework for Graph-based Urban Area Delineation and Analysis}, type = {Master's thesis}, year = 2024 }
In-depth Analysis of Evaluation Strategies for Time Series Synthesizers. Stenger, Michael; (2023, April). University of Wuerzburg, Wuerzburg.
- [ BibTeX ]
@mastersthesis{thesis_stenger, address = {Wuerzburg}, author = {Stenger, Michael}, keywords = {descartes}, month = {04}, school = {University of Wuerzburg}, title = {In-depth Analysis of Evaluation Strategies for Time Series Synthesizers}, year = 2023 }

Publications

Journal and Magazine Articles

Master Thesis

Data privacy protection

Data privacy protection

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