Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in this paper, a combination-model-based approach is proposed by combining a similarity-measurement-based method and a model-based method for anomaly detection. First, the process of data representation is performed to generate a new data form to arrive at the purpose of reducing data volume. Furthermore, due to the anomalies being generally caused by changes in amplitude and shape, we take both the original time series data and their amplitude change data into consideration of the process of data representation to capture the shape and morphological features. Then, the results of data representation are employed to establish a model for anomaly detection. Compared with the state-of-the-art methods, experimental studies on a large number of datasets show that the proposed method can significantly improve the performance of anomaly detection with higher data anomaly resolution.

由于时间序列数据具有数据量大和不平稳的特点，因此通常无法通过基于单模型的方法检测时间序列数据中的异常来获得令人满意的结果。为了解决这个问题，本文提出了一种基于组合模型的方法，将基于相似度测量的方法与基于模型的方法相结合进行异常检测。首先，执行数据表示的过程以生成新的数据形式，以达到减少数据量的目的。此外，由于异常通常是由幅度和形状的变化引起的，因此我们将原始时间序列数据及其幅度变化数据都考虑到数据表示过程中以捕获形状和形态特征。然后，数据表示的结果用于建立异常检测模型。与最新方法相比，对大量数据集的实验研究表明，该方法可以以更高的数据异常分辨率显着提高异常检测的性能。