Mining recent temporal patterns for event detection in multivariate time series data

TLDR

Pattern mining techniques are actively researched to improve classifier performance. The study introduces a recent temporal pattern mining framework to find predictive patterns for monitoring and event detection in complex multivariate time series. The framework converts time series into interval sequences of temporal abstractions and builds complex backward‑in‑time patterns using temporal operators. Applied to 13,558 diabetic patients, the framework efficiently discovers patterns that aid detection and diagnosis of diabetes‑related adverse conditions.

Abstract

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes.