Multivariate Time Series Classification Using Temporal Metafeature Abstractions
Tamara B. Sipes, Ph.D.
Space Plasma Physics Lab, UCSD
SciberQuest, Inc.
Extraction of knowledge from massive and complex data sets poses a major obstacle to scientific progress, even more so when the data is in the form of time series. We demonstrate a new approach to the classification of multivariate time series data by utilizing an innovative feature extraction technique in combination with a specialized data mining algorithm. The technique extracts global features and metafeatures in order to capture the necessary time-lapse information. The features are then used to create a static, intermediate data set that includes all the important time-varying information and is suitable for analysis using the standard supervised data mining techniques. The viability of the new algorithm called MineTool-TS is demonstrated through its application to the problem of automatic detection of flux transfer events in spacecraft data and mining of simulation data. The technique has also been successfully applied to a variety of medical, biomedical, environmental and space physics data.
Tamara B. Sipes, Ph.D. is a researcher at the Space Physics lab at UCSD specializing in data mining, predictive modeling and computational algorithms. She has a substantial industry and research experience in building highly innovative predictive models and creative solutions to complex problems. Her data mining expertise has been applied to a variety of data, industries and areas, including space physics, biotechnology, scientific, financial, robotic, educational and signal processing data. Her work has led to several patent applications and published research in the areas of data mining and learning technologies. In addition to her position at SciberQuest, Inc. Dr. Sipes is also an instructor at the University of California San Diego Extension where she created and teaches several Data Mining courses.
Besides her appointment at UCSD, Dr. Sipes is a Vice President of Analytics as SciberQuest, Inc. a company specializing in providing advanced solutions to the most complex computational and data analysis challenges facing the scientific world, including the development of self-adaptive algorithms for modeling of complex, multi-scale problems, computational infrastructure for NASA's magnetospheric virtual observatory , specialized scientific simulations for the Air Force including a first-ever, multi-resolution, 3D, parallel, object-oriented, electromagnetic PIC (particle in cell) code capable of handling complex boundaries, as well as advanced data mining methods for predictive analytics of static, time series, image and simulation data. Dr. Sipes earned her Ph.D. in Computer Science from Vanderbilt University.