Journal
APPLIED INTELLIGENCE
Volume 51, Issue 7, Pages 4874-4887Publisher
SPRINGER
DOI: 10.1007/s10489-020-02041-3
Keywords
Time series data; Anomaly detection; Data representation; The Markov model; Combination model
Categories
Funding
- Raytheon Chair for Systems Engineering
- Fundamental Research Funds for the Central Universities [K50510040013, K5051304007]
- Natural Science Foundation of China [61374068]
- Science and Technology Development Fund, MSAR [078/2015/A3]
- Doctoral Students' Short-Term Study Abroad Scholarship Fund of Xidian University
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This study proposes a combined-model approach for anomaly detection in time series data, which utilizes data representation to capture shape and morphological features, leading to significantly improved performance in data anomaly detection compared to state-of-the-art methods.
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.
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