Accurate Changing Point Detection for l(1) Mean Filtering
It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data whil...
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| Veröffentlicht in: | IEEE signal processing letters Jg. 23; H. 2; S. 297 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.02.2016
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| Schlagworte: | |
| ISSN: | 1558-2361, 1070-9908 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on l(1) filtering suffer from the occurrence of false changing points in the estimate. This is known as the staircase effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect. |
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| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/LSP.2016.2517605 |