Anomaly Detection in High Dimensional Data
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- Peter Burridge & A. M. Robert Taylor, 2006. "Additive Outlier Detection Via Extreme‐Value Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 685-701, September.
- Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
- Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017.
"Visualising forecasting algorithm performance using time series instance spaces,"
International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
- Yanfei Kang & Rob J. Hyndman & Kate Smith-Miles, 2016. "Visualising forecasting Algorithm Performance using Time Series Instance Spaces," Monash Econometrics and Business Statistics Working Papers 10/16, Monash University, Department of Econometrics and Business Statistics.
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More about this item
Keywords
data stream; high-dimensional data; nearest neighbour searching; unsupervised outlier detection;All these keywords.
JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2019-10-28 (Computational Economics)
- NEP-ORE-2019-10-28 (Operations Research)
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