NVAR: Nonlinear

Vector Autoregression Models (original) (raw)

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) doi:10.1038/s41467-021-25801-2.

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

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.