A trend-based segmentation method and the support vector regression for financial time series forecasting
type: Item
instance of: scholarly article
title: A trend-based segmentation method and the support vector regression for financial time series forecasting
zbMATH Open document ID: 1264.91097
DOI: 10.1155/2012/615152
author: Jheng-Long Wu
author: Pei-Chann Chang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: We present a novel trend-based segmentation method (TBSM) and the support vector regression (SVR) for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation (PLR), has been applied to locate a set of trading points within a financial time series data. However, owing to the dynamics in stock trading, PLR cannot reflect the trend changes within a specific time period. Therefore, a trend based segmentation method is developed in this research to overcome this issue. The model is tested using various stocks from America stock market with different trend tendencies. The experimental results show that the proposed model can generate more profits than other models. The model is very practical for real-world application, and it can be implemented in a real-time environment.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 91G70
zbMATH DE Number: 6173425
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/615152
OpenAlex ID: W2153779067
Wikidata QID: Q58912308
cites work: Q4856771
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description: scientific article; zbMATH DE number 6173425