cross_validate: Time series cross-validation with DP-INAR model predictions

In bayesianfactory/dpinar: DP-INAR model package

Description

Obtain predictions of a given test set and the mean absolute errors

Usage

cross_validate(time_series, p = 1, h = 1, training_epoch = NULL,
  prior = list(a_alpha = NULL, a0_tau = NULL, b0_tau = NULL, a0_G0 =
  NULL, b0_G0 = NULL, lambda_max = NULL), burn_in = 10^3,
  chain_length = 10^4, random_seed = 1761, verbose = TRUE)

Arguments

time_series	A univariate time series.
h	Number of steps ahead to be predicted.
training_epoch	The last observation of the first training set.
prior	List of prior hyperparameters where: a_alpha Hyperparameters of the thinning component. a0_tau, b0_tau Hyperparameters of the concentration parameter Gamma prior. a0_G0, b0_G0 Base measure hyperparameters. lambda_max Hyperparameter of the uniform distribution that minimizes the corresponding D-KL.
burn_in	Number of iterations for the "burn-in" period which are discarded in the chain.
chain_length	Number of iterations of the chain.
random_seed	Value of the random seed generator.
verbose	If TRUE log info is provided.

Value

A list with the following elements:

est

Predictions of the test set.

mae

Mean Absolute Error of the test set predictions.

bayesianfactory/dpinar documentation built on July 18, 2019, 1:27 a.m.