Two step ahead autoregressive prediction
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Is it possible to use the AR function in Matlab to train models such as:
y(t+2)=a(1)u(t-1)+a(2)u(t-2)+...+a(p)u(t-p)
rather than:
y(t+1)=a(1)u(t-1)+a(2)u(t-2)+...+a(p)u(t-p)
I want to avoid predicting y(t+2) using y(t+1).
Many thanks
1 commentaire
Ganesh
le 29 Nov 2023
If you would like to predict y(t+2), you can use the Nonconsecutive Lags parameter and tweak the array indices to achieve the result. However, by skipping a term in the middle might stand as a blocker to predict the subsequent terms of the sequence. Kindly ensure that all terms of the sequence are sequentially computed to avoid miscalculations.
Thank you,
Ganesh Saravanan
Réponses (1)
Divyam
le 21 Août 2024 à 15:40
Hi @Elias Pergantis, yes, you can utilize the AR functions to train models which have non-consecutive lags between terms using the 'ARLag' parameter of the 'regARIMA' function. Here is a sample code for the same.
% Sample Model Equation: y(t) = 0.25*u(t-3) + 0.1*u(t-4) + 0.05*u(t-5)
% The 'AR' parameter sets the coefficients of the u(t-k) terms
Mdl = regARIMA('AR', {0.25, 0.1, 0.05}, 'ARLags', [3,4,5])
% 'ARLag' parameter specifies that nonzero AR coefficients exist at lags t-3, t-4, and t-5
Mdl.AR
For more information regarding how to model AR models with Nonconsecutive Lags, you can refer to this documentation: https://www.mathworks.com/help/econ/specification-for-regression-models-with-ar-errors.html#:~:text=using%20dot%20notation.-,AR%20Error%20Model%20with%20Nonconsecutive%20Lags,-Try%20This%20Example
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