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Hierarchical Piecewise Linear Approximation A Novel Representation of Time Series Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bettaiah, Vineetha Ranganath, Heggere S. |
| Copyright Year | 2014 |
| Abstract | This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the timeamplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA achieves dimensionality reduction while preserving prominent local features and general shape of the time series. The HPLA permits coarse-fine processing, allows flexible definition of similarity between two time series based on mathematical measures or general time series shape, and supports query by content, clustering and classification based on whole or subsequence similarity. Keywords-Data Mining; Dimensionality Reduction; Piecewise Linear Representation; Time Series Representation. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.thinkmind.org/download.php?articleid=dbkda_2014_5_40_50101 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
