Skip to main content

Research Repository

Advanced Search

A novel symbolization technique for time-series outlier detection

Smith, Gavin; Goulding, James

Authors

GAVIN SMITH GAVIN.SMITH@NOTTINGHAM.AC.UK
Assistant Professor in Business Analytic



Abstract

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where speed and storage costs can be reduced is via symbolization as a pre-processing step, additionally opening up the use of an array of discrete algorithms. With this common pre-processing step in mind, this work highlights that (1) existing symbolization approaches are designed to address problems other than outlier detection and are hence sub-optimal and (2) use of off-the-shelf symbolization techniques can therefore lead to significant unnecessary data corruption and potential performance loss when outlier detection is a key aspect of the data mining task at hand. Addressing this a novel symbolization method is motivated specifically targeting the end use application of outlier detection. The method is empirically shown to outperform existing approaches.

Publication Date Oct 29, 2015
Peer Reviewed Peer Reviewed
Book Title 2015 IEEE International Conference on Big Data (Big Data)
APA6 Citation Smith, G., & Goulding, J. (2015). A novel symbolization technique for time-series outlier detection. In 2015 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData.2015.7364037
DOI https://doi.org/10.1109/BigData.2015.7364037
Keywords Detection; Preprocessing; Symbolization; Quantization; Optimization; Time series; Data mining
Publisher URL https://ieeexplore.ieee.org/document/7364037/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Files

noval symbolization.pdf (325 Kb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





You might also like



Downloadable Citations

;