Time-Series Learning Using Monotonic Logical Properties

Marcell Vazquez-Chanlatte, Shromona Ghosh, Jyotirmoy V. Deshmukh, Alberto L. Sangiovanni-Vincentelli, and Sanjit A. Seshia. Time-Series Learning Using Monotonic Logical Properties. In 18th International Conference on Runtime Verification (RV), pp. 389–405, November 2018.

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Abstract

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 non-trivial 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 classifiers/monitors for slow-downs and traffic jams.

BibTeX

@inproceedings{vazquez-rv18,
  author    = {Marcell Vazquez{-}Chanlatte and
               Shromona Ghosh and
               Jyotirmoy V. Deshmukh and
               Alberto L. Sangiovanni{-}Vincentelli and
               Sanjit A. Seshia},
  title     = {Time-Series Learning Using Monotonic Logical Properties},
  booktitle = {18th International Conference on Runtime Verification (RV)},
  pages     = {389--405},
  month  = "November",
  year      = {2018},
  abstract  = {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 non-trivial 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 classifiers/monitors for slow-downs and traffic jams.},
}