Detrending time series: Difference between revisions
From Atomix
Creating new page |
mNo edit summary |
||
| Line 8: | Line 8: | ||
|year=2007 | |year=2007 | ||
|doi=10.1073/pnas.0701020104 | |doi=10.1073/pnas.0701020104 | ||
}}</ref> | }}</ref>. The following techniques can be used for detrending <ref name="Wuetal_PNAS"/>: | ||
The following techniques can be used for detrending <ref name=" | |||
* Linear trend removal | * Linear trend removal | ||
* Low-pass filtering using moving averages or linear filters (e.g., butterworth filter) | * Low-pass filtering using moving averages or linear filters (e.g., butterworth filter) | ||
* Empirical modal decomposition | * Empirical modal decomposition | ||
Revision as of 17:12, 29 November 2021
In the context of analysing turbulence observations, detrending typically refers to removing the low-frequency signal such that the signal retains contributions from turbulence and surface waves (if present). In ocean environments, this time scale is usually of the order of minutes.
There is no exact definition for what consists of a "trend", nor any set algorithm for identifying the trend [1]. The following techniques can be used for detrending [1]:
- Linear trend removal
- Low-pass filtering using moving averages or linear filters (e.g., butterworth filter)
- Empirical modal decomposition
- ↑ 1.0 1.1 Zhaohua Wu, Norden E. Huang, Steven R. Long and and Chung-Kang Peng. 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series. PNAS. doi:10.1073/pnas.0701020104
