  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.
