rolling_origin: Assessing forecasting accuracy with rolling origin

In tsfknn: Time Series Forecasting Using Nearest Neighbors

Assessing forecasting accuracy with rolling origin

Description

It uses the model and the time series associated with the knnForecast object to asses the forecasting accuracy of the model using the last h values of the time series to build test sets applying a rolling origin evaluation.

Usage

rolling_origin(knnf, h = NULL, rolling = TRUE)

Arguments

knnf	A knnForecast object.
h	A positive integer. The forecast horizon. If NULL the prediction horizon of the knnForecast object is used.
rolling	A logical. If TRUE (the default), forecasting horizons from 1 to h are used. Otherwise, only horizon h is used.

Details

This function assesses the forecast accuracy of the model used by the knnForecast object. It uses h different test and training sets. The first test set consists of the last h values of the time series (the training set is formed by the previous values). The next test set consists of the last h - 1 values of the time series and so on (the last test set is formed by the last value of the time series).

Value

A list containing at least the following fields:

test_sets	a matrix containing the test sets used in the evaluation. Every row contains a different test set.
predictions	The predictions for the test sets.
errors	The errors for the test sets.
global_accu	Different measures of accuracy applied to all the errors.
h_accu	Different measures of accuracy applied to all the errors for every forecasting horizon.

Examples

pred <- knn_forecasting(UKgas, h = 4, lags = 1:4, k = 2)
ro <- rolling_origin(pred)
print(ro$global_accu)

tsfknn documentation built on Sept. 4, 2023, 9:06 a.m.