tEDM (version 1.1)

simplex: simplex forecast

Description

simplex forecast

Usage

# S4 method for data.frame
simplex(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 1,
  k = E + 1,
  dist.metric = "L1",
  dist.average = TRUE,
  threads = length(E)
)
# S4 method for list
simplex(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 1,
  k = E + 1,
  dist.metric = "L1",
  dist.average = TRUE,
  threads = length(E)
)

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

tau

step of time lag

Arguments

data

observation data.

column

name of library variable.

target

name of target variable.

lib

(optional) libraries indices.

pred

(optional) predictions indices.

E

(optional) embedding dimensions.

tau

(optional) step of time lags.

k

(optional) number of nearest neighbors used in prediction.

dist.metric

(optional) distance metric (L1: Manhattan, L2: Euclidean).

dist.average

(optional) whether to average distance.

threads

(optional) number of threads to use.

References

Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
simplex(sim,"x","y",E = 4:10,k = 7,threads = 1)