generate_brownian_bridge

Generate a functional time series from an iid Brownian Bridge process.
If \(W(t)\) is a Wiener process, the Brownian Bridge is
defined as \(W(t) - tW(1)\). Each functional observation is discretized on
the points indicated in <code>v</code>.

Analyze functional data and its change points. Includes
functionality to store and process data, summarize and validate assumptions,
characterize and perform inference of change points, and provide
visualizations. Data is stored as discretely collected observations without
requiring the selection of basis functions. For more details see chapter 8
of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers
are forthcoming. Focused works are also included in the documentation of
corresponding functions.

Jeremy VanderDoes

fChange

Functional Change Point Detection and Analysis

Gregory Rice

Martin Wendler

generate_brownian_bridge function

<dl><dt>N</dt>
<dd>Numeric. The number of observations for the generated data.</dd>
<dt>v</dt>
<dd>Numeric (vector). Discretization points of the curves.This can be
the evaluated points or the number of evenly spaced points on [0,1].
By default it is evenly spaced on [0,1] with 30 points.</dd>
<dt>sd</dt>
<dd>Numeric. Standard deviation of the Brownian Motion process.
The default is <code>1</code>.</dd></dl>

Arguments

Generate a Brownian Bridge Process — generate_brownian_bridge

<dl>

<dt>N</dt>
<dd>Numeric. The number of observations for the generated data.</dd>


<dt>v</dt>
<dd>Numeric (vector). Discretization points of the curves.This can be
the evaluated points or the number of evenly spaced points on [0,1].
By default it is evenly spaced on [0,1] with 30 points.</dd>


<dt>sd</dt>
<dd>Numeric. Standard deviation of the Brownian Motion process.
The default is <code>1</code>.</dd>

</dl>

generate_brownian_bridge: Generate a Brownian Bridge Process

Description

Usage

Value

Arguments

Examples