AMP: a new time-frequency feature extraction method for intermittent time-series data
The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, th...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Online Access: | https://eprints.nottingham.ac.uk/52186/ |
| _version_ | 1848798668306513920 |
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| author | Barrack, Duncan S. Goulding, James Hopcraft, Keith Preston, Simon Smith, Gavin |
| author_facet | Barrack, Duncan S. Goulding, James Hopcraft, Keith Preston, Simon Smith, Gavin |
| author_sort | Barrack, Duncan S. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, these approaches are not always appropriate for intermittent time-series data, where intermittency is characterized by constant values for large periods of time punctuated by sharp and transient increases or decreases in value.
Motivated by this, we present aggregation, mode decomposition and projection (AMP) a feature extraction technique particularly suited to intermittent time-series data which contain time-frequency patterns. For our method all individual time-series within a set are combined to form a non-intermittent aggregate. This is decomposed into a set of components which represent the intrinsic time-frequency signals within the data set. Individual time-series can then be _t to these components to obtain a set of numerical features that represent their intrinsic time-frequency patterns. To demonstrate the effectiveness of AMP, we evaluate against the real word task of clustering intermittent time-series data. Using synthetically generated data we show that a clustering approach which uses the features derived from AMP significantly outperforms traditional clustering methods. Our technique is further exemplified on a real world data set where AMP can be used to discover groupings of individuals which correspond to real world sub-populations. |
| first_indexed | 2025-11-14T20:23:26Z |
| format | Conference or Workshop Item |
| id | nottingham-52186 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:23:26Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-521862020-05-04T17:15:20Z https://eprints.nottingham.ac.uk/52186/ AMP: a new time-frequency feature extraction method for intermittent time-series data Barrack, Duncan S. Goulding, James Hopcraft, Keith Preston, Simon Smith, Gavin The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, these approaches are not always appropriate for intermittent time-series data, where intermittency is characterized by constant values for large periods of time punctuated by sharp and transient increases or decreases in value. Motivated by this, we present aggregation, mode decomposition and projection (AMP) a feature extraction technique particularly suited to intermittent time-series data which contain time-frequency patterns. For our method all individual time-series within a set are combined to form a non-intermittent aggregate. This is decomposed into a set of components which represent the intrinsic time-frequency signals within the data set. Individual time-series can then be _t to these components to obtain a set of numerical features that represent their intrinsic time-frequency patterns. To demonstrate the effectiveness of AMP, we evaluate against the real word task of clustering intermittent time-series data. Using synthetically generated data we show that a clustering approach which uses the features derived from AMP significantly outperforms traditional clustering methods. Our technique is further exemplified on a real world data set where AMP can be used to discover groupings of individuals which correspond to real world sub-populations. 2015-08-10 Conference or Workshop Item PeerReviewed Barrack, Duncan S., Goulding, James, Hopcraft, Keith, Preston, Simon and Smith, Gavin (2015) AMP: a new time-frequency feature extraction method for intermittent time-series data. In: 1st International Workshop on Mining and Learning from Time Series (MiLeTS), 10-13 August 2015, Sydney, Australia. time-series feature extraction intermittence |
| spellingShingle | time-series feature extraction intermittence Barrack, Duncan S. Goulding, James Hopcraft, Keith Preston, Simon Smith, Gavin AMP: a new time-frequency feature extraction method for intermittent time-series data |
| title | AMP: a new time-frequency feature extraction method for
intermittent time-series data |
| title_full | AMP: a new time-frequency feature extraction method for
intermittent time-series data |
| title_fullStr | AMP: a new time-frequency feature extraction method for
intermittent time-series data |
| title_full_unstemmed | AMP: a new time-frequency feature extraction method for
intermittent time-series data |
| title_short | AMP: a new time-frequency feature extraction method for
intermittent time-series data |
| title_sort | amp: a new time-frequency feature extraction method for
intermittent time-series data |
| topic | time-series feature extraction intermittence |
| url | https://eprints.nottingham.ac.uk/52186/ |