电子学报2018，Vol.46Issue(2)：325-332,8.DOI:10.3969/j.issn.0372-2112.2018.02.010
基于层级实时记忆算法的时间序列异常检测算法
Time Series Anomaly Detection Model Based on Hierarchical Temporal Memory
摘要
Abstract
Time series anomaly detection is an important area of data mining.Traditional methods of time series a-nomaly detection usually find the surprise,outlier,etc.,by comparing the data with the historical data.However,there are some limits with these methods,such as the inaccurate separation of the sequence,the false decision of the state and the win-dow size or the incorrect definition and judgement of the anomaly.This paper proposes a time series anomaly detection mod-el based on hierarchical temporal memory (HTM) to overcome the shortages of the traditional methods.This method can recognize and learn the intrinsic patterns in the time series and build a prediction model to determine an anomaly by compa-ring the real value with the predicted one.First,sparse distributed representation (SDR) is used to represent the raw data;then,the SDR is entered into the HTM model to make prediction;lastly,the proposed model evaluates the data by computing the difference of the actual value and the predicted one.The experiments on the artificial data and the real data show that HTM can detect anomalies accurately and quickly.关键词
异常检测/神经网络/层级实时记忆/稀疏离散表征Key words
anomaly detection/neuron network/hierarchical temporal memory/sparse distributed representation分类
信息技术与安全科学引用本文复制引用
曾惟如,吴佳,闫飞..基于层级实时记忆算法的时间序列异常检测算法[J].电子学报,2018,46(2):325-332,8.基金项目
国家自然科学基金(No.61503059,No.61403316) （No.61503059,No.61403316）
