SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

TLDR

The authors introduce SwiftRule, a temporal data‑mining technique that extracts human‑readable classification rules for time‑series analysis. SwiftRule segments time series into short intervals, models short‑term trends with polynomial functions, and uses these trend sequences as rule premises to classify data, supported by a novel distance metric and a dynamic classifier derived from a static one. The method enables rapid, one‑pass segmentation, anomaly detection, and is effective on benchmark series such as Lorenz attractors, building energy consumption, and ECG data, making it suitable for real‑time applications.

Abstract

In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.