Time Series Learning using Monotonic Logical Properties
This addresses the need for automated learning methods in cyber-physical systems, such as traffic monitoring, but appears incremental as it builds on existing logical and machine learning techniques.
The authors tackled the problem of discovering logical structure in time-series data from cyber-physical systems by proposing a logic-based framework that embeds domain knowledge into parametric formulas, enabling the use of off-the-shelf machine learning tools for labeling and extracting logical specifications, and demonstrated it on real-world traffic data to learn classifiers for slow-downs and traffic jams.
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 boundaries 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.