simplex forecast

Usage

# S4 method for class '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 class '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)
)

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.

Value

A list

xmap: forecast performance
varname: name of target variable
method: method of cross mapping
tau: step of time lag

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)
#> The suggested E and k for variable y is 10 and 7