Time series anomaly detection is an important area of data mining. Traditional methods of time series anomaly 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 window size or the incorrect definition and judgement of the anomaly. This paper proposes a time series anomaly detection model 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 comparing 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.