Abstract
In this paper the approaches to processing temporal data is considered. The problem of the anomaly detection among sets of time series is setting up. The algorithms TS-ADEEP and TS-ADEEP-Multi for anomaly detection in time series sets for the case when the learning set contains examples of several classes are proposed. The method for improving the accuracy of anomaly detection, due to “compression” of these time series is used.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vagin, V., Golovina, E., Zagoryanskaya, A., Fomina, M.: Exact and plausible inference in intelligent systems. In: Vagin, V., Pospelov, D. (eds.) 712 p. FizMatLit, Moscow (2008). (in Russian)
Roddick, J.F., Spiliopoulou, M.: A bibliography of temporal, spatial and spatio-temporal data mining research. SIGKDD Explor. Newsl. 1(1), 34–38 (1999). http://doi.acm.org/10.1145/846170.846173
Lin, W., Orgun, M.A., Williams, G.J.: An overview of temporal data mining. In: Proceedings of the 1st Australasian Data Mining Workshop, Sydney, Australia, pp. 83–90 (2002)
Antunes, C.M., Oliveira, A.L.: Temporal data mining: an overview. In: Eleventh International Workshop on the Principles of Diagnosis (2001)
Perfilieva, L., Yarushkina, N., Afanasieva, T., Romanov, A.: Time series analysis using soft computing methods. Int. J. Gen. Syst. 42(6), 687–705 (2013)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection - a surey. ACM Comput. Surv. 41(3), 1–72 (2009)
Arning, A., Agrawal, R., Raghavan P.: A linear method for deviation detection in large databases. In: Proceedings of KDD 1996, pp. 164–169 (1996)
Antipov, S., Fomina, M.: Problem of anomalies detection in time series sets. Prog. Prod. Syst. (2), 78–82 (2012). (in Russian)
Fomina, M., Antipov, S., Vagin, V.: Methods and algorithms of anomaly searching in collections of time series. In: Proceedings of the first International Scientific Conference Intelligent Information Technologies for Industry (IITI 2016), vol. 1, pp. 63–73. In Series Advances in Intelligent Systems and Computing, vol. 450. Springer (2016)
Saito, N.: Local feature extraction and its application using a library of bases. Ph.D. thesis, Yale University, December 1994. 244 p
Pham, D.T., Chan, A.B.: Control chart pattern recognition using a new type of self organizing neural network. Proc. Instn. Mech. Engrs. 212(1), 115–127 (1998)
UCI Repository of Machine Learning Datasets. http://archive.ics.uci.edu/ml/
Antipov, S.G., Vagin, V.N., Fomina, M.V.: Detection of data anomalies at network traffic analysis. In: Open Semantic Technologies for Intelligent Systems - Conference Proceedings, Minsk, Belarus, pp. 195–198 (2017)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)
Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T., Zaccaria, R.: Analysis of human behavior recognition algorithms based on acceleration data. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1602–1607 (2013)
Yanping, C., Eamonn, K., Bing, H., et al.: The UCR Time Series Classification Archive–2015, July 2015. www.cs.ucr.edu/~eamonn/time_series_data
Olszhewski, R.: Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. Ph.D thesis. School of Computer Science, Carnegie Mellon University, Pittsburgh (2001). 125 p
Acknowledgment
This work was supported by grants from the Russian Foundation for Basic Research № 15-01-05567, 17-07-00442.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Antipov, S.G., Vagin, V.N., Morosin, O.L., Fomina, M.V. (2019). The Problem of the Anomaly Detection in Time Series Collections for Dynamic Objects. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-01821-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-01821-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01820-7
Online ISBN: 978-3-030-01821-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)