tsEvaTransformSeriesToStatSeasonal_ciPercentile

This function decomposes a time series into a season-dependent trend and a season-dependent standard deviation.
The season-dependent amplitude is given by a seasonal factor multiplied by a slowly varying percentile.

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

tsEvaTransformSeriesToStatSeasonal_ciPercentile function

<dl><dt>timeStamps</dt>
<dd>A vector of time stamps for the time series.</dd>
<dt>series</dt>
<dd>The original time series.</dd>
<dt>timeWindow</dt>
<dd>The length of the moving window used for trend estimation.</dd>
<dt>percentile</dt>
<dd>The percentile value used for computing the slowly varying percentile.</dd></dl>

Arguments

season-dependent standard deviation.
The season-dependent standard deviation is given by a seasonal factor
multiplied by a slowly varying standard deviation.
transformation non stationary -&gt; stationary
x(t) = (y(t) - trend(t) - ssn_trend(t))/(stdDev(t)*ssn_stdDev(t))
transformation stationary -&gt; non stationary
y(t) = stdDev(t)*ssn_stdDev(t)*x(t) + trend(t) + ssn_trend(t)
trasfData.trendSeries = trend(t) + ssn_trend(t)
trasfData.stdDevSeries = stdDev(t)*ssn_stdDev(t)

this function decomposes the series into a season-dependent trend and a

tsEvaTransformSeriesToStatSeasonal_ciPercentile — tsEvaTransformSeriesToStatSeasonal_ciPercentile

<dl>

<dt>timeStamps</dt>
<dd>A vector of time stamps for the time series.</dd>


<dt>series</dt>
<dd>The original time series.</dd>


<dt>timeWindow</dt>
<dd>The length of the moving window used for trend estimation.</dd>


<dt>percentile</dt>
<dd>The percentile value used for computing the slowly varying percentile.</dd>

</dl>

tsEvaTransformSeriesToStatSeasonal_ciPercentile: tsEvaTransformSeriesToStatSeasonal_ciPercentile

Description

Usage

Value

Arguments

this function decomposes the series into a season-dependent trend and a

Examples