TSPred (version 5.0)

MSE: MSE error of prediction

Description

The function calculates the MSE error between actual and predicted values.

Usage

MSE(actual, prediction)
NMSE_eval(eval_par = list(train.actual = NULL))
RMSE_eval()
MAPE_eval()
sMAPE_eval()
MAXError_eval()
AIC_eval()
BIC_eval()
AICc_eval()
LogLik_eval()

Arguments

actual

A vector or univariate time series containing actual values for a time series that are to be compared against its respective predictions.

prediction

A vector or univariate time series containing time series predictions that are to be compared against the values in actual.

eval_par

List of named parameters required by NMSE such as train.actual.

Value

A numeric value of the MSE error of prediction.

Error metrics

Normalised Mean Squared Error.

Root Mean Squared Error.

Mean Absolute Percentage Error.

Symmetric Mean Absolute Percentage Error.

Maximal Error.

Fitness criteria

Akaike's Information Criterion.

Schwarz's Bayesian Information Criterion.

Second-order Akaike's Information Criterion.

Log-Likelihood.

References

Z. Chen and Y. Yang, 2004, Assessing forecast accuracy measures, Preprint Series, n. 2004-2010, p. 2004-10.

See Also

NMSE,MAPE,sMAPE, MAXError

Examples

# NOT RUN {
data(SantaFe.A,SantaFe.A.cont)
pred <- marimapred(SantaFe.A,n.ahead=100)
MSE(SantaFe.A.cont[,1], pred)

# }