smap: smap forecast

In tEDM: Temporal Empirical Dynamic Modeling

smap forecast

Description

smap forecast

Usage

## S4 method for signature '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)
)

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.

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

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)

tEDM documentation built on Aug. 25, 2025, 5:12 p.m.