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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/1519019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555049465675776 |
|---|---|
| author | Shiwei Su |
| author_facet | Shiwei Su |
| author_sort | Shiwei Su |
| collection | DOAJ |
| 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 |
| id | doaj-art-5d997a5c29044b16bfd8545d68a7b184 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-5d997a5c29044b16bfd8545d68a7b1842025-02-03T05:49:40ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/1519019Nonlinear ARIMA Models with Feedback SVR in Financial Market ForecastingShiwei Su0School of FinanceIn 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.http://dx.doi.org/10.1155/2021/1519019 |
| spellingShingle | Shiwei Su Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting Journal of Mathematics |
| title | 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_short | Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting |
| title_sort | nonlinear arima models with feedback svr in financial market forecasting |
| url | http://dx.doi.org/10.1155/2021/1519019 |
| work_keys_str_mv | AT shiweisu nonlineararimamodelswithfeedbacksvrinfinancialmarketforecasting |