Beta
ManageSearchChatDiscover
314444

Improving Time Series Forecasting Using a Hybrid SARIMA and Neural Network Model

Article

Last updated: 28 Dec 2024

OverviewSimilar Items

Subjects

-

Tags

-

Abstract

A hybrid forecasting model was proposed in this article; seasonal time series ARIMA and neural network back propagation (BP) models were combined together in which is known as SARIMABP. This model was used to improve forecasting of high frequency data with application on exchange rate (Egyptian pound / US dollar). The aim is to combine models to build a complete picture especially if a time series exhibits different patterns. The forecasting performance was compared among SARIMABP and SARIMA models and showed that the mean square error (MSE) and the mean absolute error (MAE) of the SARIMABP model were the lowest. The turning point evaluations also show that the proposed model has the ability to capture the actual direction of turning points of the time series.  

DOI

10.21608/esju.2014.314444

Keywords

ARIMA - Back Propagation - Foreign Exchange - High Frequency - Neural Network, SARIMA, SARIMABP - Time Series

Volume

58

Article Issue

2

Related Issue

43135

Issue Date

2014-12-01

Publish Date

2014-12-01

Page Start

118

Page End

133

Print ISSN

0542-1748

Online ISSN

2786-0086

Link

https://esju.journals.ekb.eg/article_314444.html

Detail API

https://esju.journals.ekb.eg/service?article_code=314444

Order

1

Type

Original Article

Type Code

1,914

Publication Type

Journal

Publication Title

The Egyptian Statistical Journal

Publication Link

https://esju.journals.ekb.eg/

MainTitle

Improving Time Series Forecasting Using a Hybrid SARIMA and Neural Network Model

Details

Type

Article

Created At

28 Dec 2024