IMPROVING FORECASTING PERFORMANCE OF FINANCIAL VARIABLES BY INTEGRATING LINEAR AND NONLINEAR ARIMA AND ARTIFICIAL NEURAL NETWORKS (ANNS) MODELS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KHASHEI M.; BIJARI M.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF SUSTAINABLE GROWTH AND DEVELOPMENT (THE ECONOMIC RESEARCH) Year:2008 | Volume:8 | Issue:2 Page(s): 83-100

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,323

Download:

Cites:

Information Journal Paper

Title

IMPROVING FORECASTING PERFORMANCE OF FINANCIAL VARIABLES BY INTEGRATING LINEAR AND NONLINEAR ARIMA AND ARTIFICIAL NEURAL NETWORKS (ANNS) MODELS

Author(s)

KHASHEI M. | BIJARI M. | Issue Writer Certificate

Keywords

FORECASTINGQ3

ARIMA MODELQ3

ARTIFICIAL NEURAL NETWORKS (ANNS) MODELQ3

HYBRID METHODSQ2

EXCHANGE RATEQ2

Abstract

The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of FORECASTING financial variables. Some alternative FORECASTING methods, based on the literature review, have been developed, which can be particularly useful in the analysis of financial time series. Despite of the numerous time series FORECASTING models, the accuracy of time series FORECASTING is fundamental to many decision processes. Selecting an efficient technique in unique situations is very difficult task for forecasters. Many researchers have integrated linear and nonlinear methods in order to yield more accurate results.In practice, it is difficult to determine the time series under study are generated from a linear or nonlinear underlying process while many aspects of economic behavior may not be pure linear or nonlinear. Although both ARIMA and Artificial Neural Networks (ANNs) models have the flexibility in modeling a variety of problems, none of which is universally the best model used indiscriminately in every FORECASTING situation.In this paper, based on the foundations of ARIMA and ANNs models, a hybrid method is proposed to forecast EXCHANGE RATE. Empirical results indicate that integrating linear and nonlinear ARIMA and Artificial Neural Networks (ANNs) models can be an effective way to improve FORECASTING accuracy Achieved by either of the above linear and nonlinear models used separately.

Cites

References

No record.

Cite

APA: Copy

KHASHEI, M., & BIJARI, M.. (2008). IMPROVING FORECASTING PERFORMANCE OF FINANCIAL VARIABLES BY INTEGRATING LINEAR AND NONLINEAR ARIMA AND ARTIFICIAL NEURAL NETWORKS (ANNS) MODELS. JOURNAL OF SUSTAINABLE GROWTH AND DEVELOPMENT (THE ECONOMIC RESEARCH), 8(2), 83-100. SID. https://sid.ir/paper/86424/en

Vancouver: Copy

KHASHEI M., BIJARI M.. IMPROVING FORECASTING PERFORMANCE OF FINANCIAL VARIABLES BY INTEGRATING LINEAR AND NONLINEAR ARIMA AND ARTIFICIAL NEURAL NETWORKS (ANNS) MODELS. JOURNAL OF SUSTAINABLE GROWTH AND DEVELOPMENT (THE ECONOMIC RESEARCH)[Internet]. 2008;8(2):83-100. Available from: https://sid.ir/paper/86424/en

IEEE: Copy

M. KHASHEI, and M. BIJARI, “IMPROVING FORECASTING PERFORMANCE OF FINANCIAL VARIABLES BY INTEGRATING LINEAR AND NONLINEAR ARIMA AND ARTIFICIAL NEURAL NETWORKS (ANNS) MODELS,” JOURNAL OF SUSTAINABLE GROWTH AND DEVELOPMENT (THE ECONOMIC RESEARCH), vol. 8, no. 2, pp. 83–100, 2008, [Online]. Available: https://sid.ir/paper/86424/en

Related Journal Papers