FcVARIMA: Forecasting with Vector Autoregessive Moving Average Model...

In Mthrun/TSAT: Time Series Analysis Tools (TSAT)

Forecasting with Vector Autoregessive Moving Average Model with difference filter

Description

VARMA and VARIMA modelling for multivariate Forecasting

Usage

FcVARIMA(Data, ARp, i = 1, MAq, ForecastHorizont = 14, PlotIt = TRUE, Time)

Arguments

Data	matrix [1:n,1:d]
ARp	numerical value, see example
i	numerical value, either zero: weakly stationary time series or 1 for non stationary
MAq	numerical value, see example
ForecastHorizont	scalar 'f', forcasting period
PlotIt	TRUE: Evaluation plots
Time	Optional, for evaluation plots

Details

Please read [Tsay, 2013].

Value

List V with

Model	List with Model: Model output of VARMA OptimizedModel: Further optimized Model of Model using refVARMA
Train	[1:(N-f),1:d] Training data for building the model
Test	[(N-f+1):N,1:d] Evaluation Data of the Model
Forecast	[(N-f+1):N,1:d] Prediction of the Model

Note

Wrapper for VARMA

Author(s)

Michael Thrun

References

[Tsay, 2013] Tsay, R. S.: Multivariate time series analysis: with R and financial applications, John Wiley & Sons, ISBN: 978-1-118-61790-8, 2013.

See Also

VARMA

Examples

#Defines p and q
MTS::Eccm(Data)

Forecast=FcVARIMA(Data,p,0,q)#If weakly stationary

Mthrun/TSAT documentation built on Feb. 5, 2024, 11:15 p.m.