Support Vector Machine and Granular Computing Based Time Series Volatility Prediction
With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2022/4163992 |
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| _version_ | 1849304843581128704 |
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| author | Yuan Yang Xu Ma |
| author_facet | Yuan Yang Xu Ma |
| author_sort | Yuan Yang |
| collection | DOAJ |
| description | With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model. |
| format | Article |
| id | doaj-art-3fb6cc63f70e4c1e914ce41f02e80449 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-3fb6cc63f70e4c1e914ce41f02e804492025-08-20T03:55:36ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/4163992Support Vector Machine and Granular Computing Based Time Series Volatility PredictionYuan Yang0Xu Ma1School of Mathematics and Computer ScienceSchool of Mathematics and Computer ScienceWith the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.http://dx.doi.org/10.1155/2022/4163992 |
| spellingShingle | Yuan Yang Xu Ma Support Vector Machine and Granular Computing Based Time Series Volatility Prediction Journal of Robotics |
| title | Support Vector Machine and Granular Computing Based Time Series Volatility Prediction |
| title_full | Support Vector Machine and Granular Computing Based Time Series Volatility Prediction |
| title_fullStr | Support Vector Machine and Granular Computing Based Time Series Volatility Prediction |
| title_full_unstemmed | Support Vector Machine and Granular Computing Based Time Series Volatility Prediction |
| title_short | Support Vector Machine and Granular Computing Based Time Series Volatility Prediction |
| title_sort | support vector machine and granular computing based time series volatility prediction |
| url | http://dx.doi.org/10.1155/2022/4163992 |
| work_keys_str_mv | AT yuanyang supportvectormachineandgranularcomputingbasedtimeseriesvolatilityprediction AT xuma supportvectormachineandgranularcomputingbasedtimeseriesvolatilityprediction |