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141480

Locally Weighted Learning for ARMA Time Series.

Article

Last updated: 22 Jan 2023

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Tags

Mechanical Power Engineering

Abstract

This paper deals with the application of locally weighted learning for forecasting time series corresponding to a wide range of ARMA(p,q) models. The objective of this paper is to explore the feasibility of locally weighted learning in time series forecasting. The study adopted a simulation approach to generate random samples corresponding to different time series models. The samples were divided into two sets: training and test sets. The training set was used to estimate the parameters of the locally weighted learning whereas the test set was used to test its performance. The results of the locally weighted learning were compared to those obtained from using Box-Jenkins modeling approach. The results of the study show that locally weighted learning outperforms Box-Jenkins modeling approach based on the criteria used which are mean squared error (MSE), mean absolute error (MAE) and ratio of the estimated data points closer to actual data points (Ratio). 

DOI

10.21608/bfemu.2021.141480

Authors

First Name

Gamal

Last Name

Al-Shawadfi

MiddleName

-

Affiliation

College of Business and Economics, King Saud University, Al-Qasseem Branch, Al-Melaida Saudia Arabia

Email

-

City

-

Orcid

-

First Name

Hindi

Last Name

Al-Hindi

MiddleName

A.

Affiliation

College of Business and Economics, King Saud University, Al-Qasseem Branch, Al-Melaida Saudia Arabia

Email

-

City

-

Orcid

-

Volume

28

Article Issue

3

Related Issue

20837

Issue Date

2003-09-01

Receive Date

2003-03-19

Publish Date

2021-01-20

Page Start

1

Page End

10

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 28_Issue 3_Pages 1-10.pdf

PDF . 192.9kB

Link

https://bfemu.journals.ekb.eg/article_141480.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=141480

Order

8

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

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-

Details

Type

Article

Created At

22 Jan 2023