Discovery of Ill–Known Motifs in Time Series Data

Sahar Deppe studied Electrical Engineering and Information Technology at Halmstad University (Halmstad, Sweden) and the OWL University of Applied Sciences and Arts (Lemgo, Germany), where she received her Master degree. From 2013 to 2020 she was employed at the Institute Industrial IT (inIT) as a research associate and during this time she completed her doctorate (Dr. rer. nat.) in cooperative graduation with Paderborn University. Since 2020 she is employed at the Fraunhofer Institute IOSB-INA as a research associate with project management responsibilities.In her dissertation, she proposed a novel method to detect motifs in time series data based on mathematical theories suited to represent and handle ill-known motifs such as invariant theory and theories in signal processing such as wavelet theory. Her research interests include but are not limited to the area of motif discovery and time series analysis, pattern recognition, and machine learning. She has published and presented her research at numerous conferences and journals such as IEEE, IARIA, PESARO where she got the best paper award for her research in motif discovery in image data.

“The book under review provides one such vantage point, and anyone whose work involves finding patterns in large amounts of data should take heed. … For those well versed in the mathematics of harmonics and waves, the book should prove very useful in showing how these theories can be applied to data series. But even those who are not specialists in this area, such as myself, can still gain many ideas from this useful tome.” (Eugene Callahan, Computing Reviews, October 11, 2022)

• Introduction.- Preliminaries.- General Principles of Time Series Motif Discovery.- State of the Art in Time Series Motif Discovery.- Distortion-Invariant Motif Discovery.- Evaluation.- Conclusion and Outlook.- Appendices A-D.