auto.fit: Automated estimation of nonlinear time series regression

Description

This function estimates nonlinear time series regression by sieve methods with chosen bases.

Usage

auto.fit(
  ts,
  c,
  d,
  b_time,
  b_timese,
  mp_type,
  type,
  ops,
  per = 0,
  k = 0,
  fix_num = 0,
  r = 1,
  s = 1,
  upper = 10
)

Value

If "nfix" is selected, the function returns a list where each element is a matrix representing the estimation function in two dimensions. Otherwise, if "nfix" is not selected, the function returns a list where each element is a vector representing the estimation function.

Arguments

ts: ts is the data set which is a time series data typically
c: the maximum value of number of basis for time input
d: the maximum value of number of basis for variate input
b_time: type of basis for time input
b_timese: type of basis for variate input
mp_type: select type of mapping function, "algeb" indicates algebraic mapping on the real line. "logari" represents logarithmic mapping on the real line
type: select type of estimation."nfix" refers to no fix estimation. "fixt" indicates fix time t estimation. "fixx" represents fix variate estimation
ops: Criteria for choosing the number of bases are provided by the package, offering four options: "AIC," "BIC," "CV," and "Kfold," each corresponding to a specific Criteria
per: the percentage for test set used in cross validation option "CV"
k: the number of fold used in k-fold cross validation "Kfold"
fix_num: fix_num indicates the use of fixed-value nonlinear time series regression. The default value is 0, which is employed for non-fixed estimation. If "fixt" is chosen, it represents a fixed time value. Otherwise, if not selected, it pertains to a fixed variate value
r: indicates number of variate
s: s is a positive scaling factor, the default is 1
upper: upper The upper bound for the variate basis domain. The default value is 10. When "algeb" or "logari" is chosen, the domain is automatically set from -upper to upper