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 A hybrid model of least squares support vector regression and particle swarm optimization for Vietnam stock market analysis
Tác giả hoặc Nhóm tác giả: Thuy-Linh Le and Thi Thu Ha Truong
Nơi đăng: The 8th Conference on Information Technology and Its Application; Số: 2;Từ->đến trang: 9-16;Năm: 2019
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Machine learning techniques have been successfully applied in time series data prediction. In this paper, a combination of least squares support vector regression (LSSVR) and particle swarm optimization (PSO) is used to forecast stock prices in Vietnam stock market. The PSO is adopted to optimize the hyperparameters of the LSSVR for improving the forecast accuracy. The proposed model (PSO-LSSVR) is validated by two financial time series data, including the daily VN-INDEX 100 and the daily stock price of Gas Joint Stock Company (PGC). The forecast accuracy of the PSO-LSSVR is compared with that of the autoregressive integrated moving average (ARIMA) and the LSSVR via performance measures, including mean absolute error (MAE) and mean absolute percentage error (MAPE). The experimental results show that the predictive ability of the PSO-LSSVR outperformed that of the ARIMA and the LSSVR both datasets. This finding provides a promising solution in predicting financial time series data.
ABSTRACT
Machine learning techniques have been successfully applied in time series data prediction. In this paper, a combination of least squares support vector regression (LSSVR) and particle swarm optimization (PSO) is used to forecast stock prices in Vietnam stock market. The PSO is adopted to optimize the hyperparameters of the LSSVR for improving the forecast accuracy. The proposed model (PSO-LSSVR) is validated by two financial time series data, including the daily VN-INDEX 100 and the daily stock price of Gas Joint Stock Company (PGC). The forecast accuracy of the PSO-LSSVR is compared with that of the autoregressive integrated moving average (ARIMA) and the LSSVR via performance measures, including mean absolute error (MAE) and mean absolute percentage error (MAPE). The experimental results show that the predictive ability of the PSO-LSSVR outperformed that of the ARIMA and the LSSVR both datasets. This finding provides a promising solution in predicting financial time series data.
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