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Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]

This paper introduces the vulgarized network autoregressive process with Gaussian and Student-t random noises. The processes relate the time-varying series of a given variable to the immediate past of the same phenomenon with the inclusion of its neighboring variables and networking structure. The g...

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Main Authors: Olawale Olanrewaju, Rasaki, Ranjan, Ravi Prakash, C. Chukwudum, Queensley, Olanrewaju, Sodiq Adejare
Format: Article
Language:English
Published: Universiti Teknologi MARA Press (Penerbit UiTM) 2023
Subjects:
QA Mathematics
Online Access:https://ir.uitm.edu.my/id/eprint/86387/1/86387.pdf
https://ir.uitm.edu.my/id/eprint/86387/
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spelling my.uitm.ir.863872023-10-31T17:26:49Z https://ir.uitm.edu.my/id/eprint/86387/ Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.] mjoc Olawale Olanrewaju, Rasaki Ranjan, Ravi Prakash C. Chukwudum, Queensley Olanrewaju, Sodiq Adejare QA Mathematics This paper introduces the vulgarized network autoregressive process with Gaussian and Student-t random noises. The processes relate the time-varying series of a given variable to the immediate past of the same phenomenon with the inclusion of its neighboring variables and networking structure. The generalized network autoregressive process would be fully spelt-out to contain the aforementioned random noises with their embedded parameters (the autoregressive coefficients, networking nodes, and neighboring nodes) and subjected to monthly prices of ten (10) edible cereals. Global-α of Generalized Network Autoregressive (GNAR) of order lag two, the neighbor at the time lags two and the neighbourhood nodal of zero, that is GNAR (2, [2,0]) was the ideal generalization for both Gaussian and student-t random noises for the prices of cereals, a model with two autoregressive parameters and network regression parameters on the first two neighbor sets at time lag one. GNAR model with student-t random noise produced the smallest BIC of -39.2298 compared to a BIC of -18.1683 by GNAR by Gaussian. The residual error via Gaussian was 0.9900 compared to the one of 0.9000 by student-t. Additionally, GNAR MSE for error of forecasting via student-t was 15.105% less than that of the Gaussian. Similarly, student-t-GNAR MSE for VAR was 1.59% less than that of the Gaussian-GNAR MSE for VAR. Comparing the fitted histogram plots of both the student-t and Gaussian processes, the two histograms produced a symmetric residual estimate for the fitted GNAR model via student-t and Gaussian processes respectively, but the residuals via the student-t were more evenly symmetric than those of the Gaussian. In a contribution to the network autoregressive process, the GNAR process with Student-t random noise generalization should always be favoured over Gaussian random noise because of its ability to absolve contaminations, spread, and ability to contain time-varying network measurements. Universiti Teknologi MARA Press (Penerbit UiTM) 2023-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/86387/1/86387.pdf Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]. (2023) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 8 (2): 9. pp. 1574-1588. ISSN 2600-8238
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Olawale Olanrewaju, Rasaki
Ranjan, Ravi Prakash
C. Chukwudum, Queensley
Olanrewaju, Sodiq Adejare
Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
description This paper introduces the vulgarized network autoregressive process with Gaussian and Student-t random noises. The processes relate the time-varying series of a given variable to the immediate past of the same phenomenon with the inclusion of its neighboring variables and networking structure. The generalized network autoregressive process would be fully spelt-out to contain the aforementioned random noises with their embedded parameters (the autoregressive coefficients, networking nodes, and neighboring nodes) and subjected to monthly prices of ten (10) edible cereals. Global-α of Generalized Network Autoregressive (GNAR) of order lag two, the neighbor at the time lags two and the neighbourhood nodal of zero, that is GNAR (2, [2,0]) was the ideal generalization for both Gaussian and student-t random noises for the prices of cereals, a model with two autoregressive parameters and network regression parameters on the first two neighbor sets at time lag one. GNAR model with student-t random noise produced the smallest BIC of -39.2298 compared to a BIC of -18.1683 by GNAR by Gaussian. The residual error via Gaussian was 0.9900 compared to the one of 0.9000 by student-t. Additionally, GNAR MSE for error of forecasting via student-t was 15.105% less than that of the Gaussian. Similarly, student-t-GNAR MSE for VAR was 1.59% less than that of the Gaussian-GNAR MSE for VAR. Comparing the fitted histogram plots of both the student-t and Gaussian processes, the two histograms produced a symmetric residual estimate for the fitted GNAR model via student-t and Gaussian processes respectively, but the residuals via the student-t were more evenly symmetric than those of the Gaussian. In a contribution to the network autoregressive process, the GNAR process with Student-t random noise generalization should always be favoured over Gaussian random noise because of its ability to absolve contaminations, spread, and ability to contain time-varying network measurements.
format Article
author Olawale Olanrewaju, Rasaki
Ranjan, Ravi Prakash
C. Chukwudum, Queensley
Olanrewaju, Sodiq Adejare
author_facet Olawale Olanrewaju, Rasaki
Ranjan, Ravi Prakash
C. Chukwudum, Queensley
Olanrewaju, Sodiq Adejare
author_sort Olawale Olanrewaju, Rasaki
title Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
title_short Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
title_full Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
title_fullStr Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
title_full_unstemmed Vulgarized neighboring network of multivariate autoregressive processes with Gaussian and Student-t distributed random noise / Rasaki Olawale Olanrewaju ... [et al.]
title_sort vulgarized neighboring network of multivariate autoregressive processes with gaussian and student-t distributed random noise / rasaki olawale olanrewaju ... [et al.]
publisher Universiti Teknologi MARA Press (Penerbit UiTM)
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/86387/1/86387.pdf
https://ir.uitm.edu.my/id/eprint/86387/
_version_ 1781709360219553792
score 13.211869

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