Beta
ManageSearchChatDiscover
248229

Machine Learning Approach For Small Samples ARMA Models Identification

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

-

Abstract

This paper proposes an effective machine learning
approach to identify small samples data generated from
autoregressive moving-average ARMA(p,q) models. The
theoretical and practical aspects of the proposed approach are
introduced , and its validity was evaluated by the ratio of
correct identification(CIR) .
For evaluating the validity of the proposed machine
learning approach, a simulation study was achieved. 192000
small samples were generated from ARMA(p,q) models with
different sample sizes(10,20,30) and different parameters sets
through the stationarity and invertibility regions. The ratio of
the correct identification is calculated and used for evaluating
the proposed approach. The average of CIR for all samples
was 99.3% which shows a good performance for the
proposed approach. The results also showed that the
automatic ARMA identification Is less sensitive to small
samples additionally, The proposed approach is quicker ,
automatic and more accurate alternative. A Python program
is written for doing automatic Identification using a machine
learning attached in the appendix.

DOI

10.21608/jsfc.2020.248229

Keywords

Artificial Intelligence (AI), machine learning(ML), Box-Jenkins Identification . The Neural Network Architecture

Authors

First Name

Nader

Last Name

Metwally

MiddleName

-

Affiliation

کلية التجارة بنين - جامعة الأزهر - طريق النصر - أمام قاعة المؤتمرات - مدينة نصر - القاهرة الرقم البريدي / 11751

Email

-

City

القاهرة

Orcid

-

First Name

Mohamed

Last Name

Agamy

MiddleName

-

Affiliation

کلية التجارة بنين - جامعة الأزهر - طريق النصر - أمام قاعة المؤتمرات - مدينة نصر - القاهرة الرقم البريدي / 11751

Email

-

City

القاهرة

Orcid

-

First Name

Gamal

Last Name

Alshawadfi

MiddleName

-

Affiliation

کلية التجارة بنين - جامعة الأزهر - طريق النصر - أمام قاعة المؤتمرات - مدينة نصر - القاهرة الرقم البريدي / 11751

Email

dr_gamal1@yahoo.com

City

القاهرة

Orcid

-

Volume

24

Article Issue

1

Related Issue

35489

Issue Date

2020-06-01

Publish Date

2020-06-01

Page Start

45

Page End

66

Print ISSN

1687-322X

Article File

JSFC_Volume 24_Issue 1_Pages 45-66.pdf

PDF . 1.5MB

Link

https://jsfc.journals.ekb.eg/article_248229.html

Detail API

https://jsfc.journals.ekb.eg/service?article_code=248229

Order

5

Type

المقالة الأصلية

Type Code

761

Publication Type

Journal

Publication Title

المجلة العلمية لقطاع کليات التجارة

Publication Link

https://jsfc.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023