In order to effectively analyze and build cyberphysical systems (CPS), designers today have to combat the data deluge problem, i.e., the burden of processing intractably large amounts of data produced by complex models and experiments. In this work, we utilize monotonic parametric signal temporal logic (PSTL) to design features for unsupervised classification of time series data. This enables using off-the-shelf machine learning tools to automatically cluster similar traces with respect to a given PSTL formula. We demonstrate how this technique produces interpretable formulas that are amenable to analysis and understanding using a few representative examples. We illustrate this with case studies related to automotive engine testing, highway traffic analysis, and auto-grading massively open online courses.
CITATION STYLE
Vazquez-Chanlatte, M., Deshmukh, J. V., Jin, X., & Seshia, S. A. (2017). Logical clustering and learning for time-series data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10426 LNCS, pp. 305–325). Springer Verlag. https://doi.org/10.1007/978-3-319-63387-9_15
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