2021
Temporal convolutional autoencoder for unsupervised anomaly detection in time series Journal Article
In: Applied Soft Computing, vol. 112, S. 107751, 2021.
2020
Time Series Encodings with Temporal Convolutional Networks Proceedings Article
In: Vasile, Massimiliano; Filipic, Bogdan (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), Bruxelles. Video (15 min) available at https://youtu.be/PjI4fpcTGFY, 2020.
Predictive Maintenance & Anomalie-Erkennung: Effiziente Instandhaltung mit Verfahren der KI Miscellaneous
Vortrag DEBRL2020 (Digital Exchange Bergisches Rheinland 2020), als Video über https://digital-xchange.de/streaming verfügbar, 2020.
2019
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks Proceedings Article
In: Barancíková, Petra; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award).
2018
Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf).
2017
Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017.
Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017.
Online anomaly detection on the Webscope S5 dataset: A comparative study Proceedings Article
In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017.
2011
The slowness principle: SFA can detect different slow components in nonstationary time series Journal Article
In: International Journal of Innovative Computing and Applications (IJICA), vol. 3, no. 1, S. 3–10, 2011.
2010
Comparing CI Methods for Prediction Models in Environmental Engineering Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany, no. 02/10, 2010, ISSN: 2191-365X.
Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Proceedings Article
In: 2010 International Joint Conference on Neural Networks, 2010.
How slow is slow? SFA detects signals that are slower than the driving force Proceedings Article
In: Filipic, Bogdan; Silc, Juri (Hrsg.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.
2009
How slow is slow? SFA detects signals that are slower than the driving force Technical Report
Cologne University of Applied Sciences no. 05/09, 2009, (e-print published at http://arxiv.org/abs/0911.4397).
On the numeric stability of the SFA implementation sfa-tk Technical Report
Cologne University of Applied Sciences no. 05/10, 2009, (e-print published at http://arxiv.org/abs/0912.1064).
2007
Adaptive Hierarchical Forecasting Proceedings Article
In: IEEE Fourth International Workshop on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS'2007), 2007.
2001
Revenue Management beim weltgrößten Reiseveranstalter: Der Kollege "Prognose" Journal Article
In: IS Report, Zeitschrift für betriebswirtschaftliche Informationssysteme, vol. 10, 2001.
Suchfeld
Machine Learning and Deep Learning Approaches for Multivariate Time Series Prediction and Anomaly Detection PhD Thesis
Leiden University and TH Köln, 2022, (PhD thesis).
Temporal convolutional autoencoder for unsupervised anomaly detection in time series Journal Article
In: Applied Soft Computing, vol. 112, S. 107751, 2021.
Time Series Encodings with Temporal Convolutional Networks Proceedings Article
In: Vasile, Massimiliano; Filipic, Bogdan (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), Bruxelles. Video (15 min) available at https://youtu.be/PjI4fpcTGFY, 2020.
Predictive Maintenance & Anomalie-Erkennung: Effiziente Instandhaltung mit Verfahren der KI Miscellaneous
Vortrag DEBRL2020 (Digital Exchange Bergisches Rheinland 2020), als Video über https://digital-xchange.de/streaming verfügbar, 2020.
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks Proceedings Article
In: Barancíková, Petra; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award).
Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf).
Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017.
Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation Proceedings Article
In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017.
Online anomaly detection on the Webscope S5 dataset: A comparative study Proceedings Article
In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017.
The slowness principle: SFA can detect different slow components in nonstationary time series Journal Article
In: International Journal of Innovative Computing and Applications (IJICA), vol. 3, no. 1, S. 3–10, 2011.