A Novel Anomaly Detection Algorithm Based on Trident Tree

Zhang, Chunkai; Yin, Ao; Deng, Yepeng; Tian, Panbo; Wang, Xuan; Dong, Lifeng

doi:10.1007/978-3-319-94295-7_20

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…Time series is a series of data points indexed in time order, which is widely existed in fields of medical [6,10,26], business [18], industry [20,25], cyber security [17,24] and so on. Time series mining is one of the attractive research topics and a key issue for the last decade, such as classification [22], clustering [15], anomaly detection [19,27], time series visualization [9,13]. Most of mining technologies require comparison of similarity measurement, which can effect the accuracy and efficiency of mining.…”

Section: Introductionmentioning

confidence: 99%

An Improvement of PAA on Trend-Based Approximation for Time Series

Zhang

Chen

Yin

et al. 2018

Lecture Notes in Computer Science

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Piecewise Aggregate Approximation (PAA) is a competitive basic dimension reduction method for high-dimensional time series mining. When deployed, however, the limitations are obvious that some important information will be missed, especially the trend. In this paper, we propose two new approaches for time series that utilize approximate trend feature information. Our first method is based on relative mean value of each segment to record the trend, which divide each segment into two parts and use the numerical average respectively to represent the trend. We proved that this method satisfies lower bound which guarantee no false dismissals. Our second method uses a binary string to record the trend which is also relative to mean in each segment. Our methods are applied on similarity measurement in classification and anomaly detection, the experimental results show the improvement of accuracy and effectiveness by extracting the trend feature suitably.

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