Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into...

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Autor principal:	Shiwei Su
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/5d997a5c29044b16bfd8545d68a7b184
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spelling	oai:doaj.org-article:5d997a5c29044b16bfd8545d68a7b1842021-11-22T01:10:18ZNonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting2314-478510.1155/2021/1519019https://doaj.org/article/5d997a5c29044b16bfd8545d68a7b1842021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1519019https://doaj.org/toc/2314-4785In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.Shiwei SuHindawi LimitedarticleMathematicsQA1-939ENJournal of Mathematics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Shiwei Su Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
description	In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.
format	article
author	Shiwei Su
author_facet	Shiwei Su
author_sort	Shiwei Su
title	Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
title_short	Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
title_full	Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
title_fullStr	Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
title_full_unstemmed	Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting
title_sort	nonlinear arima models with feedback svr in financial market forecasting
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5d997a5c29044b16bfd8545d68a7b184
work_keys_str_mv	AT shiweisu nonlineararimamodelswithfeedbacksvrinfinancialmarketforecasting
_version_	1718418340686331904

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

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