| dc.description.abstract | A wide variety of time series data have recently been accumulated from sensors around our daily lives, due to the rapid development of the Internet of Things (IoT) technology. As a result, demands for analyzing time series data are rapidly increasing, and anomaly detection is one of the important tasks in various demands. This paper proposes an anomaly detection framework for univariate time series data. First, the time series data are divided into three categories according to the data characteristics. The three categories of data are (1) stationary time series data, (2) periodic time series data, and (3) non-stationary and non-periodic time series data based on the Dickey-Fuller test, fast Fourier transform (FFT), and Pearson product-moment correlation coefficient. Different schemes using statistics and deep learning concepts are then applied to different categories of data for performing anomaly detection.
For stationary time series data, the ratio of the means of a large sliding time window and a small window is calculated. An anomaly is assumed to occur, if the ratio exceeds a threshold value. For periodic time series data, the period of the data is first derived. Afterwards, the standard deviation ratio of data in two consecutive periods is calculated. It is assumed that an anomaly occurs if the ratio exceeds a threshold value. For non-stationary and non-periodic time series data, the neural network of the gated recurrent unit (GRU) model is applied for predicting time series data value. The anomaly is detected on the basis of the cumulative density function of the normal distribution over prediction error.
Four open real-word datasets and an artificial dataset released on Nupic platform maintained by Numenta corporation are used for performance evaluation of the proposed framework. The evaluation results are compared with those of related methods, namely the ADSaS, STL, SARIMA, LSTM, and LSTM with STL methods. The comparisons show that the proposed framework has the best F1 score for anomaly detection.
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