Event detection in time series by genetic programming

Abstract

The aim of event detection in time series is to identify particular occurrences of user-interest in one or more time lines, such as finding an anomaly in electrocardiograms or reporting a sudden variation of voltage in a power supply. Current methods are not adequate for detecting certain kinds of events without any domain knowledge. Therefore, we propose a Genetic Programming (GP) based event detection methodology in which solutions can be built from raw time series data. The framework is applied to five synthetic data sets and one real world application. The experimental results show that working on raw data even with a dimensionality as high as 140 × 80, genetic programming can achieve superior performance to conventional methods operating on pre-defined features. Furthermore, analysis of the evolved event detectors shows that they have captured the regularities inserted into the synthetic data sets and some individuals can be readily understood by humans. Index Terms-genetic programming; feature extraction; event detection; time series.

Event prediction algorithm using neural networks for the power management system of electric vehicles

“…The algorithm models predict events whose time series values remain above or below the threshold for at least the breakeven time. Weiss and Hirsh [36] proposed a system to predict events based on two steps: (A) identifying temporal and sequential patterns within time-series data, and (B) generating an ordered list of prediction patterns from step A. Xi and Song [37] suggested an algorithm that perceives unusual variations in time-series values and classifies them as events. Guralnik and Srivastava [38] approached the event problem as changepoint detection in time-series data.…”

Automated Feature Design for Numeric Sequence Classification by Genetic Programming

“…Very recently, a GP method was proposed for time series event detection, which has many similarities to the current problem. This work primarily focused on identifying the length of an event in a sequence and finding simple features from multivariate relationships [25].…”

Time-series event-based prediction: An unsupervised learning framework based on genetic programming

“…Recently, in [37], Xie, et al used GP to detect temporal and spatial relationships in a time-series to detect events. The proposed algorithm used a sliding window mechanism that scans the given time-series sequentially.…”

“…Discovering an event means to detect unusual variations in the time-series pattern and label them as rare events. An event in a time-series is defined as "the occurrence of a variation in values over a time span that is of particular interest to a user" [37]. The focus of this paper is on time-series events detection.…”

“…The disadvantage of these techniques is that they are suitable only when rules for determining the occurrence of an event are clear and well understood. The second category is to detect changes in the flow of the time-series values and label these changes as events (e.g., see [37]). The underlying assumption of these models is that it is possible to mathematically model a time-series to detect unusual variations.…”