Authors: Raafat Talib Hashim
ABSTRACT: In real life intelligence systems, there is a steady flow of various and dynamic time-series data that describe the observed behavior over time. Although predictive or learning-based approaches are frequently employed for the analysis of such data, relatively little work has focused on descriptive behavioral analysis across domains with fundamentally different temporal semantics. We show in this study a cross-domain analysis of the behavior pattern of real-world intelligent systems from heterogeneous time series dataset coming from different operational fields.
We consider two publicly available datasets of distinct intelligent systems and temporal structures. One of the datasets represents behavior in terms of explicit timestamps, while the other captures behavioral development with an implicit sequence order. Instead of imposing artificial time alignment, using machine-learning based models or approaches for some other kind of extraneous information extraction within some complex pipeline, the analysis is purely descriptive in nature and has its roots in static temporal semantics.
For each dataset, independent non-overlapping temporal windows are constructed and interpretable window-level features are extracted to capture the magnitude, variability, directional trends, temporal texture, as well as cross-signal coherence of behavior. The learned behavioral descriptions show that a number of structural patterns hold in different domains even while data collection, temporal encoding and system contexts vary. These common patterns are described at the interpretative level (i.e., no use is made of prediction, anomaly detection or statistical significance), thus enhancing the importance on explainability to deal with heterogeneous scenarios. By showing that cross domain-behavioral regularities can be detected in a fixed time semantics, this work provides a clear analytic framework for modeling real world intelligent agents and emphasizes the role of descriptive methods as an alternative complement to model driven approaches.
Keywords: Cross-Domain Behavioral Analysis; Heterogeneous Time-Series Data; Descriptive Temporal Analysis; Temporal Feature Extraction; Explainable Behavioral Patterns.