tsEvaRunningMeanTrend

This function calculates the running mean trend of a given time series using a specified time window.

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi
available here:
<https://github.com/menta78/tsEva>. It contains an implementation of the
Transformed-Stationary (TS) methodology for non-stationary extreme
value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>.
In synthesis this approach consists in:
(i) transforming a non-stationary time series into a
stationary one to which the stationary extreme value theory can be applied; and
(ii) reverse-transforming the result into a non-stationary extreme
value distribution.
'RtsEva' offers several options for trend estimation (mean, extremes, seasonal)
and contains multiple plotting functions displaying different aspects
of the non-stationarity of extremes.

Alois Tilloy

RtsEva

Performs the Transformed-Stationary Extreme Values Analysis

tsEvaRunningMeanTrend function

<dl><dt>timeStamps</dt>
<dd>A vector of time stamps corresponding to the observations in the series.</dd>
<dt>series</dt>
<dd>A vector of numeric values representing the time series.</dd>
<dt>timeWindow</dt>
<dd>The length of the time window (in the same time units as the time stamps) used for calculating the running mean trend.</dd></dl>

Arguments

Calculate the running mean trend of a time series — tsEvaRunningMeanTrend

<dl>

<dt>timeStamps</dt>
<dd>A vector of time stamps corresponding to the observations in the series.</dd>


<dt>series</dt>
<dd>A vector of numeric values representing the time series.</dd>


<dt>timeWindow</dt>
<dd>The length of the time window (in the same time units as the time stamps) used for calculating the running mean trend.</dd>

</dl>

tsEvaRunningMeanTrend: Calculate the running mean trend of a time series

Description

Usage

Value

Arguments

Examples