Anomaly Subsequence Detection with Dynamic Local Density for Time Series

TL;DR

This paper introduces a novel anomaly subsequence detection method for time series that uses dynamic local density estimation and ensemble learning to improve accuracy without losing trend information.

Contribution

It proposes a new approach combining dynamic segmentation with ensemble learning to enhance anomaly detection in high-dimensional time series.

Findings

Outperforms state-of-the-art methods in accuracy

Effective in high-dimensional and diverse datasets

Maintains trend information during detection

Abstract

Anomaly subsequence detection is to detect inconsistent data, which always contains important information, among time series. Due to the high dimensionality of the time series, traditional anomaly detection often requires a large time overhead; furthermore, even if the dimensionality reduction techniques can improve the efficiency, they will lose some information and suffer from time drift and parameter tuning. In this paper, we propose a new anomaly subsequence detection with Dynamic Local Density Estimation (DLDE) to improve the detection effect without losing the trend information by dynamically dividing the time series using Time Split Tree. In order to avoid the impact of the hash function and the randomness of dynamic time segments, ensemble learning is used. Experimental results on different types of data sets verify that the proposed model outperforms the state-of-art methods, and the accuracy has big improvement.