Stock market trend prediction using AHP and weighted kernel LS-SVM
| Autor: | Ivana P. Markovic, Milena Stankovic, Jelena Stanković, Miloš B. Stojanović |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
business.industry Analytic hierarchy process Feature selection 02 engineering and technology computer.software_genre Machine learning Theoretical Computer Science Support vector machine 020901 industrial engineering & automation Feature (computer vision) Polynomial kernel Kernel (statistics) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Stock market Geometry and Topology Artificial intelligence Data mining business computer Software Mathematics |
| Zdroj: | Soft Computing. 21:5387-5398 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-016-2123-0 |
| Popis: | Nowadays, stock market trend prediction represents a challenging subject both in terms of the choice of the prediction model and in terms of constructing the set of features that model will use for prediction. To address both of these aspects, we propose a feature ranking and feature selection approach in combination with weighted kernel least squares support vector machines (LS-SVMs). We introduce the analytic hierarchy process (AHP) into the stock market and propose evaluation criteria which provide the prediction model with relevant knowledge of the underlying processes of the studied stock market. The feature weights obtained by the AHP method are used for feature ranking and selection, and used with the LS-SVMs through a weighted kernel. The test results indicate that the proposed model outperforms the benchmark models. In addition, the set of feature weights obtained by the proposed approach can also independently be incorporated into other kernel-based learners. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink