TSPred: Functions for Benchmarking Time Series Prediction

Description Usage Arguments Value Author(s) See Also Examples

evaluate is a generic function for evaluating the quality of time series prediction or modeling fitness based on a particular metric defined in an evaluating object. The function invokes particular methods which depend on the class of the first argument.

evaluate(obj, ...)

## S3 method for class 'evaluating'
evaluate(obj, test, pred, ...)

## S3 method for class 'fitness'
evaluate(obj, mdl, test = NULL, pred = NULL, ...)

## S3 method for class 'error'
evaluate(obj, mdl = NULL, test = NULL, pred = NULL, ..., fitness = FALSE)

`obj`	An object of class `evaluating` defining a particular metric.
`...`	Other parameters passed to `eval_func` of `obj`.
`test`	A vector or univariate time series containing actual values for a time series that are to be compared against `pred`.
`pred`	A vector or univariate time series containing time series predictions that are to be compared against the values in `test`.
`mdl`	A time series model object for which fitness is to be evaluated.
`fitness`	Should the function compute the fitness quality? If `TRUE` the function uses `mdl` to compute fitness error, otherwise, it uses `test` and `pred` to compute prediction error. For `evaluate.fitness`, `test` and `pred` are ignored and can be set to `NULL`. For `evaluate.error`, `mdl` is ignored if `fitness` is `FALSE`, otherwise, `test` and `pred` are ignored and can be set to `NULL`.

A list containing obj and the computed metric values.

Rebecca Pontes Salles

Other evaluate: evaluate.tspred()

data(CATS,CATS.cont)
mdl <- forecast::auto.arima(CATS[,1])
pred <- forecast::forecast(mdl, h=length(CATS.cont[,1]))

evaluate(MSE_eval(), test=CATS.cont[,1], pred=pred$mean)
evaluate(MSE_eval(), mdl, fitness=TRUE)
evaluate(AIC_eval(), mdl)