EXPARMAfit: Fitting of Exponential Autoregressive Moving Average...

In EXPARMA: Fitting of Exponential Autoregressive Moving Average (EXPARMA) Model

Fitting of Exponential Autoregressive Moving Average (EXPARMA) Model

Description

This function will fit EXPARMA model for given parameters.

Usage

EXPARMAfit(ts_data, order, par)

Arguments

ts_data	a univarite time series data
order	order represents the values of autoregresive (p) and moving average (q) parameters of EXPARMA model. p and q will take integer values of 1 or more. By default these values are set as 1.
par	par is the parametric space of the EXPARMA model. The total number of parametrs are 2*(p+q+1).

Details

This function takes 3 inputs, all compulsory, i.e., data, order and parameters of the model to be fitted and returns the fitted values, residuals, RSS and AIC for the fitted model. No optimisation is done. This function is useful for simulation of data with given order and parameters.

Value

It returns the fitted EXPARMA model.

Examples

datats=c(17597,14074,11425,11691,11298,12351,14311,
12349,10537,11755,13154,11989,13022,12107,11172,10667,
10091,12204,12274,22343)
EXPARMAfit(datats,order=c(1,1), par=c(1,0.5,0.1,0.5,0.5,0.5))

EXPARMA documentation built on July 26, 2023, 5:25 p.m.