Time-series Data Anomaly Detection Method Based on Deep Learning

With regard to the anomaly detection problem of time-series data with a skewed between-class distribution, we propose a detection method based on deep convolutional neural network.First, we employ the sampling method to preprocess the unbalanced time-series data.Second, the original time-series data are converted into a series of continuous segments with a uniform scale and consistent duration.Finally, we feed the data into a convolutional neural network model with four hidden layers for anomaly detection.The experimental results show that the proposed method covers the shortage of existing detection technologies that ignore the skewness of data distributions and results in a low-detection precision.Compared with the existing time-series classification methods, the proposed method provided a satisfactory performance.