tsEvaTransformSeriesToStationaryMMXTrend

<code>tsEvaTransformSeriesToStationaryMMXTrend</code>
transforms a time series to a stationary one by focusing on the monthly maximum values.
The trend and slowly varying amplitude are computed on the monthly maximum values.

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

tsEvaTransformSeriesToStationaryMMXTrend 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 the time series data.</dd>
<dt>timeWindow</dt>
<dd>The size of the time window used for detrending.</dd></dl>

Arguments

tsEvaTransformSeriesToStationaryMMXTrend — tsEvaTransformSeriesToStationaryMMXTrend

<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 the time series data.</dd>


<dt>timeWindow</dt>
<dd>The size of the time window used for detrending.</dd>

</dl>

tsEvaTransformSeriesToStationaryMMXTrend: tsEvaTransformSeriesToStationaryMMXTrend

Description

Usage

Value

Arguments

See Also

Examples