Abstract
Time series analysis is a part of data mining and nowadays an important field of research due to the increasing amount of data that is recorded sequentially by various systems. Especially the identification of anomalous subsequences arouses great interest, since a manual search for errors or malfunctions is not possible in most cases. Often outliers are defined as points or sequences that deviate significantly from the course of one or multiple time series, yet there are also applications where the trend rather than the exact course of time series is relevant. In that case, there is an approach of clustering the time series per time point and analyzing their cluster transitions over time. Sequences that change their cluster members suddenly or often, indicate an anomaly.
In 2019, a novel approach for the detection of these transition-based outliers was introduced [19]. Now, we present an algorithm called DACT (Detecting Anomalies based on Cluster Transitions) that is able to identify outlier sequences of the same type. It is a simple approach that stands out due to different results, although a similar type of anomalies is targeted. In the evaluation, we examine and discuss the differences. Our experiments show, that the results are competitive and reasonable.
Keywords
- Outlier detection
- Time series analysis
- Clustering
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Global economy, world economy. https://www.theglobaleconomy.com/
Ahmar, A.S., et al.: Modeling data containing outliers using ARIMA additive outlier (ARIMA-AO). J. Phy.: Conf. Ser. 954, 012010 (2018)
Chambon, S., Thorey, V., Arnal, P.J., Mignot, E., Gramfort, A.: A deep learning architecture to detect events in EEG signals during sleep. In: 28th International Workshop on Machine Learning for Signal Processing, pp. 1–6 (2018)
Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on loess (with discussion). J. Off. Stat. 6, 3–73 (1990)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Hill, D.J., Minsker, B.S.: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach. Environ. Model Softw. 25(9), 1014–1022 (2010)
Kawahara, Y., Sugiyama, M.: Change-point detection in time-series data by direct density-ratio estimation. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 389–400. SIAM (2009)
Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), pp. 226–233 (2005)
Keogh, E., Lonardi, S., Chiu, B.Y.C.: Finding surprising patterns in a time series database in linear time and space. In: Proceedings of the 8th Int. Conference on Knowledge Discovery and Data Mining, pp. 550–556 (2002)
Kieu, T., Yang, B., Jensen, C.S.: Outlier detection for multidimensional time series using deep neural networks. In: 2018 19th IEEE International Conference on Mobile Data Management (MDM), pp. 125–134 (2018)
Klassen, G., Tatusch, M., Himmelspach, L., Conrad, S.: Fuzzy clustering stability evaluation of time series. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU (2020)
Lin, J., Keogh, E., Ada Fu, Van Herle, H.: Approximations to magic: finding unusual medical time series. In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), pp. 329–334 (2005)
Liu, S., Yamada, M., Collier, N., Sugiyama, M.: Change-point detection in time-series data by relative density-ratio estimation. Neural Netw. 43, 72–83 (2013)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019)
Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2018)
Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)
Sun, P., Chawla, S., Arunasalam, B.: Mining for outliers in sequential databases. In: ICDM, pp. 94–106 (2006)
Tatusch, M., Klassen, G., Bravidor, M., Conrad, S.: Show me your friends and i’ll tell you who you are finding anomalous time series by conspicuous cluster transitions. In: Data Mining Communications in Computer and Information Science. AusDM 2019, vol. 1127, pp. 91–103 (2019)
Tatusch, M., Klassen, G., Bravidor, M., Conrad, S.: How is your team spirit? cluster over-time stability evaluation. In: Machine Learning and Data Mining in Pattern Recognition, MLDM (2020)
Zhou, Y., Zou, H., Arghandeh, R., Gu, W., Spanos, C.J.: Non-parametric outliers detection in multiple time series a case study: power grid data analysis. In: AAAI (2018)
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Tatusch, M., Klassen, G., Conrad, S. (2020). Loners Stand Out. Identification of Anomalous Subsequences Based on Group Performance. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_28
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DOI: https://doi.org/10.1007/978-3-030-65390-3_28
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