Time-series learning using monotonic logical properties
- Author(s): Vazquez-Chanlatte, M
- Ghosh, S
- Deshmukh, JV
- Sangiovanni-Vincentelli, A
- Seshia, SA
- et al.
Published Web Locationhttps://doi.org/10.1007/978-3-030-03769-7_22
© Springer Nature Switzerland AG 2018. Cyber-physical systems of today are generating large volumes of time-series data. As manual inspection of such data is not tractable, the need for learning methods to help discover logical structure in the data has increased. We propose a logic-based framework that allows domain-specific knowledge to be embedded into formulas in a parametric logical specification over time-series data. The key idea is to then map a time series to a surface in the parameter space of the formula. Given this mapping, we identify the Hausdorff distance between surfaces as a natural distance metric between two time-series data under the lens of the parametric specification. This enables embedding nontrivial domain-specific knowledge into the distance metric and then using off-the-shelf machine learning tools to label the data. After labeling the data, we demonstrate how to extract a logical specification for each label. Finally, we showcase our technique on real world traffic data to learn clas-sifiers/monitors for slow-downs and traffic jams.