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2020

Thill, Markus; Konen, Wolfgang; Bäck, Thomas

Time Series Encodings with Temporal Convolutional Networks Konferenzbeitrag

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 Sonstige

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 Konferenzbeitrag

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 Forschungsbericht

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 Konferenzbeitrag

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 Konferenzbeitrag

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 Konferenzbeitrag

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 Artikel

International Journal of Innovative Computing and Applications (IJICA), 3 (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 Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany, (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 Konferenzbeitrag

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 Konferenzbeitrag

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 Forschungsbericht

Cologne University of Applied Sciences (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 Forschungsbericht

Cologne University of Applied Sciences (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 Konferenzbeitrag

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" Artikel

IS Report, Zeitschrift für betriebswirtschaftliche Informationssysteme, 10 , 2001.

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

 


Suchfeld

Zeige alle

16 Einträge « 1 von 2 »
1. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Time Series Encodings with Temporal Convolutional Networks. 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. (Typ: Konferenzbeitrag | Links | BibTeX)
2. Thill, Markus; Konen, Wolfgang: Predictive Maintenance & Anomalie-Erkennung: Effiziente Instandhaltung mit Verfahren der KI. Vortrag DEBRL2020 (Digital Exchange Bergisches Rheinland 2020), als Video über https://digital-xchange.de/streaming verfügbar, 2020. (Typ: Sonstige | Links | BibTeX)
3. Thill, Markus; Däubner, Sina; Konen, Wolfgang; Bäck, Thomas: Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks. In: Barancíková, Petra; Holena, Martin; others, (Hrsg.): Proc. 19th Conference Information Technologies - Applications and Theory (ITAT 2019), 2019, (Best Paper Award). (Typ: Konferenzbeitrag | Links | BibTeX)
4. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions. 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). (Typ: Forschungsbericht | Links | BibTeX)
5. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017. (Typ: Konferenzbeitrag | Links | BibTeX)
6. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017. (Typ: Konferenzbeitrag | Links | BibTeX)
7. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Online anomaly detection on the Webscope S5 dataset: A comparative study. In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017. (Typ: Konferenzbeitrag | Links | BibTeX)
8. Konen, Wolfgang; Koch, Patrick: The slowness principle: SFA can detect different slow components in nonstationary time series. In: International Journal of Innovative Computing and Applications (IJICA), 3 (1), S. 3–10, 2011. (Typ: Artikel | Links | BibTeX)
9. 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. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Faculty of Computer Science and Engineering Science, Cologne University of Applied Sciences, Germany, (02/10), 2010, ISSN: 2191-365X. (Typ: Forschungsbericht | Links | BibTeX)
10. Koch, Patrick; Konen, Wolfgang; Hein, Kristine: Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap. In: 2010 International Joint Conference on Neural Networks, 2010. (Typ: Konferenzbeitrag | Links | BibTeX)
16 Einträge « 1 von 2 »