Detecting outlier samples in multivariate time series dataset

Knowledge-Based Systems

Volumn 21, Issue 8, 2008, Pages 807-812

  Weng, Xiaoqing ^a,b   Shen, Junyi ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b HEBEI UNIVERSITY OF ECONOMICS AND BUSINESS   (China)

Author keywords

Extended Frobenius norm; Multivariate time series; Outlier sample; Solving Set

Indexed keywords

BOOLEAN FUNCTIONS; COMMERCE; FEATURE EXTRACTION; INTERFACES (COMPUTER); TIME SERIES ANALYSIS;

BRAIN-COMPUTER INTERFACES; DATASETS; EXTENDED FROBENIUS NORM; FROBENIUS NORMS; K-NEAREST NEIGHBORS; MULTIVARIATE TIME SERIES; OUTLIER SAMPLE; OUTLIER SAMPLES; SOLVING SET; STOCK MARKETS; TIME COMPLEXITIES;

SET THEORY;

EID: 54949117176     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2008.03.048     Document Type: Article

References (26)

1. Takeuchi J., and Yamanishi K. A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering 18 4 (2006) 482-492
2. K. Yamanishi, J. Takeuchi, A unifying framework for detecting outliers and change-points from non-stationary time series data, in: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 676-681.
3. S. Muthukrishnan, R. Shah, J.S. Vitter, Mining deviants in time series data streams, in: Proceedings of the 16th International Conference on Scientific and Statistical Database Management, 2004, pp. 41-50.
4. Caroni C., and Karioti V. Detecting an innovative outlier in a set of time series. Computational Statistics & Data Analysis 46 (2004) 561-570
5. Liu H., Shah S., and Jiang W. On-line outlier detection and data cleaning. Computers and Chemical Engineering 28 (2004) 1635-1647
6. Perron P., and Rodriguez G. Searching for additive outliers in non-stationary time series. Journal of Time Series Analysis 24 2 (2003) 193-220
7. Cai Y., and Davies N. A simple diagnostic method of outlier detection for stationary Gaussian time series. Journal of Applied Statistics 30 2 (2003) 205-223
8. Zheng B.X., Xi Y.G., and Du X.H. Outlier minging for time series data based on outlier index. Acta Automatica Sinica 30 1 (2004) 70-77
9. Wen Q., and Peng H. Analysis of time series outlier mining based on wavelet transform. Journal of UEST of China 34 4 (2005) 556-558
10. Tsay R.S., Pena D., and Pankratz A.E. Outliers in multivariate time series. Biometrika 87 4 (2000) 789-804
11. P. Galeano, D. Peña, R.S. Tsay, Outlier detection in multivariate time series via projection pursuit, Statistics and Econometrics Working Papers ws044211, Universidad Carlos III, Departamento de Estadi{dotless}́stica y Econometri{dotless}́a, September 2004. Available from: .
12. M.E. Otey, S. Parthasarathy, A dissimilarity measure for comparing subsets of data: application to multivariate time series, in: Proceedings of ICDM 2005 Workshop on Temporal Data Mining: Algorithms, Theory and Applications held in conjunction with The Fifth IEEE International Conference on Data Mining, 2005, pp. 101-112.
13. J.C. Silva, M. Klusch, CoDOTS: confidential detection of outliers in distributed time series, in: Proceedings of ICDM 2005 Workshop on Temporal Data Mining: Algorithms, Theory and Applications held in conjunction with The Fifth IEEE International Conference on Data Mining, 2005, pp. 18-24.
14. Karioti V., and Caroni C. Detecting outlying series in sets of short time series. Computational Statistics & Data Analysis 39 (2002) 351-364
15. X.Q. Weng, J.Y. Shen, Outlier mining for multivariate time series based on local sparsity coefficient, in: Proceedings of the 6th World Congress on Intelligent Control and Automation, 2006, pp. 5957-5961.
16. K Yang, C. Shahabi, A PCA-based similarity measure for multivariate time series, in: Proceedings of the Second ACM International Workshop on Multimedia Databases, 2004, pp. 65-74.
17. K Yang, C. Shahabi, A multilevel distance-based index structure for multivariate time series, in: Proceedings of the 12th International Symposium on Temporal Representation and Reasoning, 2005, pp. 65-73.
18. K. Yang, C. Shahabi, A PCA-based kernel for kernel PCA on multivariate time series, in: Proceedings of ICDM 2005 Workshop on Temporal Data Mining: Algorithms, Theory and Applications held in conjunction with The Fifth IEEE International Conference on Data Mining, 2005, pp. 149-156.
19. Angiulli F., and Pizzuti C. Outlier mining in large high-dimensional data sets. IEEE Transactions on Knowledge and Data Engineering 17 2 (2005) 203-215
20. Angiulli F., Basta S., and Pizzuti C. Distance-based detection and prediction of outliers. IEEE Transactions on Knowledge and Data Engineering 18 2 (2006) 145-160
21. http://finance.sina.com.cn/.
22. Schlögl A., Neuper C., and Pfurtscheller G. Estimating the mutual information of an EEG-based brain-computer-interface. Biomedizinische Technik 47 1-2 (2002) 3-8
23. http://ida.first.fhg.de/projects/bci/competition_ii/.
24. C. Shahabi, D. Yan, Real-time pattern isolation and recognition over immersive sensor data streams, in: Proceedings of the 9th International Conference On Multi-Media Modeling, 2003, pp. 93-113.
25. Yang K., and Shahabi C. An efficient k nearest neighbor search for multivariate time series. Information and Computation 205 (2007) 65-98
26. M. Agyemang, C.I. Ezeife, LSC-Mine: algorithm for mining local outliers, in: Proceedings of the 15th Information Resources Management Association International Conference, 2004, pp. 5-8.