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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Using Lowly Correlated Time Series to Recover Missing Values in Time Series: A Comparison Between SVD and CD |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-3-319-22362-9 |
| ISSN | 0302-9743 |
| Page Range | 237 - 254 |
| Event Title | Advances in Spatial and Temporal Databases - 14th International Symposium, SSTD 2015, Hong Kong, China, August 26-28, 2015. Proceedings |
| Event Type | conference |
| Event Location | Hong Kong, China |
| Event Start Date | August 26 - 2015 |
| Event End Date | August 28 - 2015 |
| Series Name | Lecture Notes in Computer Science |
| Abstract Text | The Singular Value Decomposition (SVD) is a matrix decomposition technique that has been successfully applied for the recovery of blocks of missing values in time series. In order to perform an accurate block recovery, SVD requires the use of highly correlated time series. However, using lowly correlated time series that exhibit shape and/or trend similarities could increase the recovery accuracy. Thus, the latter time series could also be exploited by including them in the recovery process. In this paper, we compare the accuracy of the Centroid Decomposition (CD) against SVD for the recovery of blocks of missing values using highly and lowly correlated time series. We show that the CD technique better exploits the trend and shape similarity to lowly correlated time series and yields a better recovery accuracy. We run experiments on real world hydrological and synthetic time series to validate our results. |
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