construct_path

<p>Graph topology or adjacency matrix.</p>

nw_topo

noise

y_init

delta_time

<p>Generates a single Graph Ornstein-Uhlenbeck path with the given
network topology (<code>nw_topo</code>), noise increments (<code>noise</code>),
initial values (<code>y_init</code>) and unique time difference (<code>delta_time</code>).</p>

Statistical inference tools for network time series,
including implementation of the Graph Ornstein-Uhlenbeck ('GrOU')
model and sparse AR-like regression on irregularly-spaced data
(Courgeau and Veraart (2020) <arXiv:2005.12720>; Courgeau and Veraart
<arXiv:2008.10930>). It also handles jumps and Levy-type driving
noises through Wiener-Poisson mixtures and Generalised Hyperbolic
distributions.

Valentin Courgeau

ntwk

Statistical Inference for Network Time Series

construct_path function

Generates a single Graph Ornstein-Uhlenbeck path with the given
network topology (<code>nw_topo</code>), noise increments (<code>noise</code>),
initial values (<code>y_init</code>) and unique time difference (<code>delta_time</code>). — construct_path

Generates a single Graph Ornstein-Uhlenbeck path with the given
network topology (<code>nw_topo</code>), noise increments (<code>noise</code>),
initial values (<code>y_init</code>) and unique time difference (<code>delta_time</code>).

construct_path: Generates a single Graph Ornstein-Uhlenbeck path with the given network topology (`nw_topo`), noise increments (`noise`), initial values (`y_init`) and unique time difference (`delta_time`).

Description

Usage

Arguments

Value

Examples