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2021

Thill, Markus; Konen, Wolfgang; Wang, Hao; Bäck, Thomas

Temporal convolutional autoencoder for unsupervised anomaly detection in time series Journal Article

In: Applied Soft Computing, vol. 112, S. 107751, 2021.

Links | BibTeX | Schlagwörter: anomaly detection, deep learning, TCN, time series

2020

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Time Series Encodings with Temporal Convolutional Networks Inproceedings

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.

Links | BibTeX | Schlagwörter: anomaly detection, deep learning, TCN, time series

Thill, Markus; Konen, Wolfgang

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.

Links | BibTeX | Schlagwörter: anomaly detection, deep learning, TCN, time series

2019

Thill, Markus; Däubner, Sina; Konen, Wolfgang; Bäck, Thomas

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks Inproceedings

In: Barancíková, Petra; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award).

Links | BibTeX | Schlagwörter: anomaly detection, deep learning, learning, LSTM, time series

2018

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

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).

Links | BibTeX | Schlagwörter: anomaly detection, DWT, maximum likelihood estimation, time series, wavelet transform

2017

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation Inproceedings

In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017.

Links | BibTeX | Schlagwörter: anomaly detection, DWT, time series

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation Inproceedings

In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017.

Links | BibTeX | Schlagwörter: anomaly detection, DWT, time series

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Online anomaly detection on the Webscope S5 dataset: A comparative study Inproceedings

In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017.

Links | BibTeX | Schlagwörter: anomaly detection, time series

2011

Konen, Wolfgang; Koch, Patrick

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.

Links | BibTeX | Schlagwörter: SFA, SOMA, time series

2010

Flasch, Oliver; Bartz-Beielstein, Thomas; Davtyan, Artur; Koch, Patrick; Konen, Wolfgang; Oyetoyan, Tosin Daniel; Tamutan, Michael

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.

Links | BibTeX | Schlagwörter: CI, GP, prediction, time series

Koch, Patrick; Konen, Wolfgang; Hein, Kristine

Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Inproceedings

In: 2010 International Joint Conference on Neural Networks, 2010.

Links | BibTeX | Schlagwörter: gesture, SFA, SOMA, time series

Konen, Wolfgang; Koch, Patrick

How slow is slow? SFA detects signals that are slower than the driving force Inproceedings

In: Filipic, Bogdan; Silc, Juri (Hrsg.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.

Links | BibTeX | Schlagwörter: SFA, SOMA, time series

2009

Konen, Wolfgang

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).

Links | BibTeX | Schlagwörter: SFA, SOMA, time series

Konen, Wolfgang

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).

Links | BibTeX | Schlagwörter: SFA, SOMA, time series

2007

Engels, Christoph; Konen, Wolfgang

Adaptive Hierarchical Forecasting Inproceedings

In: IEEE Fourth International Workshop on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS'2007), 2007.

Links | BibTeX | Schlagwörter: Dato Minint, time series

2001

Konen, Wolfgang; Engels, Christoph

Revenue Management beim weltgrößten Reiseveranstalter: Der Kollege "Prognose" Journal Article

In: IS Report, Zeitschrift für betriebswirtschaftliche Informationssysteme, vol. 10, 2001.

Links | BibTeX | Schlagwörter: Data Mining, optimization, time series

 


Suchfeld

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18 Einträge « 1 von 2 »
1.

Thill, Markus

Machine Learning and Deep Learning Approaches for Multivariate Time Series Prediction and Anomaly Detection PhD Thesis

Leiden University and TH Köln, 2022, (PhD thesis).

BibTeX

2.

Thill, Markus; Konen, Wolfgang; Wang, Hao; Bäck, Thomas

Temporal convolutional autoencoder for unsupervised anomaly detection in time series Journal Article

In: Applied Soft Computing, vol. 112, S. 107751, 2021.

Links | BibTeX

3.

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Time Series Encodings with Temporal Convolutional Networks Inproceedings

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.

Links | BibTeX

4.

Thill, Markus; Konen, Wolfgang

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.

Links | BibTeX

5.

Thill, Markus; Däubner, Sina; Konen, Wolfgang; Bäck, Thomas

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks Inproceedings

In: Barancíková, Petra; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award).

Links | BibTeX

6.

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

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).

Links | BibTeX

7.

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation Inproceedings

In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017.

Links | BibTeX

8.

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation Inproceedings

In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017.

Links | BibTeX

9.

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Online anomaly detection on the Webscope S5 dataset: A comparative study Inproceedings

In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017.

Links | BibTeX

10.

Konen, Wolfgang; Koch, Patrick

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.

Links | BibTeX

18 Einträge « 1 von 2 »