seasonalACF

Computes the empirical ACF for each season of a time series.

internal

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

Kevin Shook

CoSMoS

Complete Stochastic Modelling Solution

Simon Michael Papalexiou

Francesco Serinaldi

Filip Strnad

Yannis Markonis

seasonalACF function

<dl><dt>TS</dt>
<dd>data frame or data table with columns <code>date</code> and <code>value</code></dd>
<dt>season</dt>
<dd>character; name of a date-component function
(e.g. <code>"month"</code>)</dd>
<dt>lag.max</dt>
<dd>integer; maximum lag for the ACF (default 50)</dd></dl>

Arguments

Calculate seasonal autocorrelation function — seasonalACF

<dl>

<dt>TS</dt>
<dd>data frame or data table with columns <code>date</code> and <code>value</code></dd>


<dt>season</dt>
<dd>character; name of a date-component function
(e.g. <code>"month"</code>)</dd>


<dt>lag.max</dt>
<dd>integer; maximum lag for the ACF (default 50)</dd>

</dl>

seasonalACF: Calculate seasonal autocorrelation function

Description

Usage

Value

Arguments

See Also