MSE_eval {TSPred}

Prediction/modeling quality metrics

Description

Constructors for the evaluating class representing a time series prediction or modeling fitness quality evaluation based on particular metrics.

Usage

MSE_eval()

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

eval_par

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

Value

An object of class evaluating.

Error metrics

MSE_eval: Mean Squared Error.

NMSE_eval: Normalised Mean Squared Error.

RMSE_eval: Root Mean Squared Error.

MAPE_eval: Mean Absolute Percentage Error.

sMAPE_eval: Symmetric Mean Absolute Percentage Error.

MAXError_eval: Maximal Error.

Fitness criteria

AIC_eval: Akaike's Information Criterion.

BIC_eval: Schwarz's Bayesian Information Criterion.

AICc_eval: Second-order Akaike's Information Criterion.

LogLik_eval: Log-Likelihood.

Author(s)

Rebecca Pontes Salles

See Also

Other constructors: ARIMA(), LT(), evaluating(), modeling(), processing(), tspred()