README.md

In NVAR: Nonlinear Vector Autoregression Models

NVAR: Nonlinear Vector Autoregression Models

Estimate nonlinear vector autoregression models (also known as the next generation reservoir computing) for nonlinear dynamic systems. The algorithm was described by Gauthier et al. (2021) .

Installation

You can install the development version of NVAR from GitHub with:

# install.packages("devtools")
devtools::install_github("Sciurus365/NVAR")

Example

This is an example for the Lorenz model.

library(NVAR)

testdata <- nonlinearTseries::lorenz()
testdata <- tibble::as_tibble(testdata)
t1 <- NVAR(data = testdata, vars = c("x", "y", "z"), s = 2, k = 2, p = 2, alpha = 1e-3)
t1_sim <- sim_NVAR(t1, length = 5000)


realdata <- nonlinearTseries::lorenz(time = seq(0, 100, by = .01)) %>% tibble::as_tibble()

library(ggplot2)
ggplot(realdata) +
  geom_line(aes(x = 1:10001, y = x), color = "red", alpha = 0.4) +
  geom_line(aes(x = 1:10001, y = x), data = t1_sim, color = "blue", alpha = 0.4) +
  geom_vline(xintercept = 5000) +
  theme_bw() +
  xlim(c(4900, 8000)) +
  labs(x = "time", y = "x")

# Red line: real data.
# Blue line: simulated data with the NVAR.
# Black vertical line: when the simulation starts.

NVAR documentation built on May 29, 2024, 2:09 a.m.