bonsai_cv_models: Uses cross validation with a list of functions

In brunocarlin/forecast.bonsai: Forecasts using the method of cross-validation

Description

Commodity function to facilitate the usage of the bonsai.cv function, may be depriciated

Usage

bonsai_cv_models(y, list_functions, h, window = NULL, Start = 1,
  min_lenght = 0, max_fold = NULL)

Arguments

y	Univariate time series
list_functions	a list of functions to test
h	Forecast horizon
window	Length of the rolling window, if NULL, a rolling window will not be used.
Start	how many observations before starting to calculate errors
min_lenght	how many observations are nescessary before starting to calculate errors
max_fold	how many times should the function calculate errors

Details

Let y contain the time series y[1:T]. Then forecastfunction is applied successively to the time series y[1:t], for t=1,…,T-h, making predictions f[t+h]. The errors are given by e[t+h] = y[t+h]-f[t+h]. If h=1, these are returned as a vector, e[1:T]. For h>1, they are returned as a matrix with the hth column containing errors for forecast horizon h. The first few errors may be missing as it may not be possible to apply forecastfunction to very short time series.

Value

Numerical time series object containing the forecast errors as a vector (if h=1) and a matrix otherwise. The time index corresponds to the last period of the training data. The columns correspond to the forecast horizons.

A matrix with the weighted object

Author(s)

Rob Hyndman created the tsCV forecast, heavily based on it

brunocarlin/forecast.bonsai documentation built on May 3, 2019, 5:45 p.m.