Abstract
This paper presents 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.
Highlights
Support vector machines SVMs have outperformed other forecasting models of machine learning or soft computing SC tools such as decision tree, neural network NN, bayes classifier, fuzzy systems FSs, evolutionary computation EC, and chaos theory by many researchers from historical nonlinear time series data applications in the last decade [1,2,3,4,5].In these techniques, many researchers presented different forecasting models in dealing with characteristics such as imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness, and low solution cost in real applications [6,7,8]
This paper proposes a forecasting framework using a Trend Based Segmentation Method (TBSM) combined with SVR model which is called TBSM-SVR trading model for stock trading
According to TBSM procedure to find turning point based on trend of stock price, we selected a time series of historical stock price in a period to segment into several segments based on three trends including uptrend, downtrend, and hold trend
Summary
Support vector machines SVMs have outperformed other forecasting models of machine learning or soft computing SC tools such as decision tree, neural network NN, bayes classifier, fuzzy systems FSs, evolutionary computation EC, and chaos theory by many researchers from historical nonlinear time series data applications in the last decade [1,2,3,4,5]. This research will consider the multiple trends of stock price’s movements in TBSM segmentation approach to capture the embedded knowledge of nonlinear time series. The TBSM approach has captured the tendency of stock price’s movement which can be inputted into SVR in learning the historical knowledge of the time series data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
