A novel symbolization technique for time-series outlier detection
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 symbol...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2015
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/52309/ |
| _version_ | 1848798696128380928 |
|---|---|
| author | Smith, Gavin Goulding, James |
| author_facet | Smith, Gavin Goulding, James |
| author_sort | Smith, Gavin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:23:52Z |
| format | Conference or Workshop Item |
| id | nottingham-52309 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:23:52Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-523092020-05-04T17:18:35Z https://eprints.nottingham.ac.uk/52309/ A novel symbolization technique for time-series outlier detection Smith, Gavin Goulding, James 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. 2015-10-29 Conference or Workshop Item PeerReviewed 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. Detection; Preprocessing; Symbolization; Quantization; Optimization; Time series; Data mining https://ieeexplore.ieee.org/document/7364037/ 10.1109/BigData.2015.7364037 10.1109/BigData.2015.7364037 10.1109/BigData.2015.7364037 |
| spellingShingle | Detection; Preprocessing; Symbolization; Quantization; Optimization; Time series; Data mining Smith, Gavin Goulding, James A novel symbolization technique for time-series outlier detection |
| title | A novel symbolization technique for time-series outlier detection |
| title_full | A novel symbolization technique for time-series outlier detection |
| title_fullStr | A novel symbolization technique for time-series outlier detection |
| title_full_unstemmed | A novel symbolization technique for time-series outlier detection |
| title_short | A novel symbolization technique for time-series outlier detection |
| title_sort | novel symbolization technique for time-series outlier detection |
| topic | Detection; Preprocessing; Symbolization; Quantization; Optimization; Time series; Data mining |
| url | https://eprints.nottingham.ac.uk/52309/ https://eprints.nottingham.ac.uk/52309/ https://eprints.nottingham.ac.uk/52309/ |