Anomaly Subsequence Detection with Dynamic Local Density for Time Series
This work addresses anomaly detection in time series data, which is crucial for applications like monitoring and fault diagnosis, but it appears incremental as it builds on existing techniques with ensemble learning and dynamic segmentation.
The paper tackles the problem of detecting anomalous subsequences in time series data, which often suffers from high computational cost and information loss in traditional methods, and proposes a Dynamic Local Density Estimation (DLDE) approach that improves accuracy without losing trend information, with experimental results showing it outperforms state-of-the-art methods with significant accuracy gains.
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.