Cited By
View all- He BGong ELi LYang Y(2023)A Stock Price Prediction Method based on LSTM and K-MeansFrontiers in Science and Engineering10.54691/fse.v3i6.51213:6(44-57)Online publication date: 20-Jun-2023
Application of TWSVR Models in Stock Price Forecast
Pages 29 - 32
Abstract
Stock price forecasting is a challenging task. Stock prices are predicted by Twin Support Vector Regression (TWSVR) with two different kernel functions in this paper. The two kernel functions are linear kernel function and polynomial kernel function. The parameters of TWSVR models were selected by genetic algorithm (GA). With the optimized parameters, these models are used to predict the closing prices of the stock in the next day. The predicted results are compared with those obtained by traditional SVR models. The results shown that the TWSVR model with polynomial kernel function has higher accuracy than twin support vector regression with linear kernel. The time consumed by TWSVR is less than that of traditional SVR in prediction.
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Index Terms
- Application of TWSVR Models in Stock Price Forecast
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September 2019
803 pages
ISBN:9781450372985
DOI:10.1145/3366194
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Published: 20 September 2019
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RICAI 2019
RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
September 20 - 22, 2019
Shanghai, China
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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
Overall Acceptance Rate 140 of 294 submissions, 48%
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View all- He BGong ELi LYang Y(2023)A Stock Price Prediction Method based on LSTM and K-MeansFrontiers in Science and Engineering10.54691/fse.v3i6.51213:6(44-57)Online publication date: 20-Jun-2023
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