tEDM (version 1.1)

smap: smap forecast

Description

smap forecast

Usage

# S4 method for data.frame
smap(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 1,
  k = E + 1,
  dist.metric = "L1",
  dist.average = TRUE,
  theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
    4, 6, 8),
  threads = length(theta)
)

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

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.

theta

(optional) weighting parameter for distances.

threads

(optional) number of threads to use.

References

Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688):477-495.

Examples

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