MAE: MAE - Mean Absolute Error

In jdestefani/UEMTS: Univariate Error Measures for Time Series forecasting

Description

MAE = 1/n ∑_{t=0}^n |y_t - y_hat_t|

Usage

MAE(y, y_hat, result_type = c("measure", "raw"))

Arguments

y	True values of the time series
y_hat	Forecasted values of the time series
result_type	String indicating whether the function should return the raw values ("raw"), or the computed measure ("measure")

Value

According to the value of result_type:

• measure -> MAE computed according to the formula

• raw -> Vector containing one MAE for each time step

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688.

Examples

y <- AirPassengers
splitting_point <- round(2*length(y)/3)
y_train <- y[1:splitting_point]
h <- 5
y_test <- y[(splitting_point+1):(splitting_point+h)]
y_hat_naive <- rep(tail(y_train,1),h)
result <- MAE(y_test,y_hat_naive)

jdestefani/UEMTS documentation built on Dec. 20, 2021, 10:05 p.m.