A novel symbolization technique for time-series outlier detection

Smith, Gavin and Goulding, James (2015) A novel symbolization technique for time-series outlier detection. In: 2015 IEEE International Conference on Big Data, Oct 29 - Nov 1 2015, Santa Clara, California, USA.

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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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/762986
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.
Keywords: Detection; Preprocessing; Symbolization; Quantization; Optimization; Time series; Data mining
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
Identification Number: https://doi.org/10.1109/BigData.2015.7364037
Depositing User: Eprints, Support
Date Deposited: 08 Jun 2018 07:54
Last Modified: 04 May 2020 17:18
URI: http://eprints.nottingham.ac.uk/id/eprint/52309

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