Detrending time series: Difference between revisions

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In the context of analysing turbulence observations, detrending yields a signal that retains mostly contributions from turbulence and surface waves (if present). The resulting detrended signal may contain contributions from frame interference (wakes), vibrations and measurement noise.
In the context of analysing turbulence observations, the resulting detrended signal should contain mostly contributions from turbulence and surface waves (if present). However, frame interference (wakes), vibrations and measurement noise may also contaminate the detrended signal.
 
 
 
There is no exact definition for what consists of a "trend", nor any set algorithm for identifying the trend <ref name="Wuetal_PNAS">{{Cite journal
|authors=Zhaohua Wu, Norden E. Huang, Steven R. Long, and Chung-Kang Peng
|journal_or_publisher=PNAS
|paper_or_booktitle=On the trend, detrending, and variability of nonlinear and nonstationary time series
|year=2007
|doi=10.1073/pnas.0701020104
}}</ref>. The following techniques can be used for detrending <ref name="Wuetal_PNAS"/>:
* Linear trend removal
* Low-pass filtering  using moving averages or linear filters (e.g., butterworth filter)
* Empirical modal decomposition
 
 
[[File:Long timeseries.png|400px|thumb|caption]]
 
==Notes==

Revision as of 17:34, 29 November 2021


Short definition of Detrending time series
Detrending typically refers to removing the low-frequency content of the time series

This is the common definition for Detrending time series, but other definitions maybe discussed within the wiki.


In the context of analysing turbulence observations, the resulting detrended signal should contain mostly contributions from turbulence and surface waves (if present). However, frame interference (wakes), vibrations and measurement noise may also contaminate the detrended signal.