Abstract
Many time series anomaly detection algorithms are hard to be applied in real scenarios for two reasons. Firstly some of them are supervised since training data is required to define the normal behavior, but it is expensive to annotate the normal part for large volume data. Secondly, many algorithms are parameter-laden, which are hard to be generalized to different dataset. This paper is motivated to overcome these disadvantages. It is believed that a normal behavior is a subsequence which is similar to some subsequences in a time series while an anomaly is a subsequence which is distinct from the others. In order to improve the efficiency of searching anomaly, we first select candidate anomalies rather than check all subsequences. We roughly distinguish the candidate anomalies from normal subsequences by transforming each subsequence into a string. If a string corresponds to only one subsequence, then it is a candidate anomaly. And the subsequences of the same string represent a kind of normal behavior. Secondly, similarity threshold is calculated according to the similarity between normal behaviors. If the similarity between a candidate anomaly and its nearest neighbor is lower than the threshold, then this candidate is determined to be anomalous. We conduct extensive experiments on benchmark datasets from diverse domains and compare our method with the state-of-the-art method. The empirical results show that our method can reach high detection rate in an unsupervised and parameter-lite manner.
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References
Keogh, E., Lin, J., Fu, A.: HOT SAX: finding the most unusual time series subsequence: algorithms and applications. In: Proceedings of International Conference on Data Mining, pp. 226–233 (2005)
Jones, M., Nikovski, D., Imamura, M., et al.: Exemplar learning for extremely efficient anomaly detection in real-valued time series. Data Min. Knowl. Disc. 30(6), 1427–1454 (2016)
Liu, B., Chen, H., Sharma, A., et al.: Modeling heterogeneous time series dynamics to profile big sensor data in complex physical systems. In: Proceedings of IEEE International Conference on Big Data, pp. 631–638 (2013)
Keogh, E., Lonardi, S., Ratanamahatana, C.: Towards parameter-free data mining. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215 (2004)
Ma, J., Perkins, S.: Online novelty detection on temporal sequences. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 613–618 (2003)
Pankaj, M., Lovekesh, V., Gautam, S., et al.: Long short term memory networks for anomaly detection in time series. In: Proceedings of European Symposium on Artificial Neural Networks, pp. 89–94 (2015)
Appice, A., Guccione, P., Malerba, D., et al.: Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Inf. Sci. 285(1), 162–180 (2014)
Laptev, N., Amizadeh, S., Flint, I.: Generic and scalable framework for automated time-series anomaly detection. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1939–1947 (2015)
Zheng, D., Li, F., Zhao, T.: Self-adaptive statistical process control for anomaly detection in time series. Expert Syst. Appl. 57(1), 324–336 (2016)
Wang, H., Tang, M., Park, Y., et al.: Locality statistics for anomaly detection in time series of graphs. IEEE Trans. Signal Process. 62(3), 703–717 (2014)
Ma, J., Sun, L., Wang, H., et al.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16(1), 4–24 (2016)
Burbeck, K., Nadjm-Tehrani, S.: Adaptive real-time anomaly detection with incremental clustering. Inf. Secur. Techn. Rep. 12(1), 56–67 (2007)
Izakian, H., Pedrycz, W.: Anomaly detection and characterization in spatial time series data: a cluster-centric approach. IEEE Trans. Fuzzy Syst. 22(6), 1612–1624 (2014)
Hsiao, K., Xu, K., Calder, J., et al.: Multicriteria similarity-based anomaly detection using pareto depth analysis. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1307–1321 (2016)
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Wang, W., Bao, J., He, H. (2019). An Unsupervised Anomaly Detection Algorithm for Time Series Big Data. In: Ren, R., Zheng, C., Zhan, J. (eds) Big Scientific Data Benchmarks, Architecture, and Systems. SDBA 2018. Communications in Computer and Information Science, vol 911. Springer, Singapore. https://doi.org/10.1007/978-981-13-5910-1_8
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DOI: https://doi.org/10.1007/978-981-13-5910-1_8
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