A feature fusion based forecasting model for financial time series.
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model i...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4074191?pdf=render |
| _version_ | 1811238126457192448 |
|---|---|
| author | Zhiqiang Guo Huaiqing Wang Quan Liu Jie Yang |
| author_facet | Zhiqiang Guo Huaiqing Wang Quan Liu Jie Yang |
| author_sort | Zhiqiang Guo |
| collection | DOAJ |
| description | Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. |
| first_indexed | 2024-04-12T12:36:21Z |
| format | Article |
| id | doaj.art-6fcb031a345b423dae70b5bee9077e19 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T12:36:21Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6fcb031a345b423dae70b5bee9077e192022-12-22T03:32:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0196e10111310.1371/journal.pone.0101113A feature fusion based forecasting model for financial time series.Zhiqiang GuoHuaiqing WangQuan LiuJie YangPredicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.http://europepmc.org/articles/PMC4074191?pdf=render |
| spellingShingle | Zhiqiang Guo Huaiqing Wang Quan Liu Jie Yang A feature fusion based forecasting model for financial time series. PLoS ONE |
| title | A feature fusion based forecasting model for financial time series. |
| title_full | A feature fusion based forecasting model for financial time series. |
| title_fullStr | A feature fusion based forecasting model for financial time series. |
| title_full_unstemmed | A feature fusion based forecasting model for financial time series. |
| title_short | A feature fusion based forecasting model for financial time series. |
| title_sort | feature fusion based forecasting model for financial time series |
| url | http://europepmc.org/articles/PMC4074191?pdf=render |
| work_keys_str_mv | AT zhiqiangguo afeaturefusionbasedforecastingmodelforfinancialtimeseries AT huaiqingwang afeaturefusionbasedforecastingmodelforfinancialtimeseries AT quanliu afeaturefusionbasedforecastingmodelforfinancialtimeseries AT jieyang afeaturefusionbasedforecastingmodelforfinancialtimeseries AT zhiqiangguo featurefusionbasedforecastingmodelforfinancialtimeseries AT huaiqingwang featurefusionbasedforecastingmodelforfinancialtimeseries AT quanliu featurefusionbasedforecastingmodelforfinancialtimeseries AT jieyang featurefusionbasedforecastingmodelforfinancialtimeseries |