Academic Journal of Computing & Information Science, 2022, 5(10); doi: 10.25236/AJCIS.2022.051003.
Minsong Gao1, Chuyu Feng2
1Department of Mathematical Sciences, Anhui University, Hefei, 230601, China
2School of International Business Administration, South China Normal University, Guangzhou, Guangdong, 510000, China
Aiming at the limitation of ARIAM model in predicting stock prices with relatively complex fluctuation trends, this paper proposes an improved ARIMA method (BMA-ARIMA) based on B-spline basis expansion and model averaging. The proposed method uses intraday prices as auxiliary information and considers its functional characteristics. By combining the two methods of parallel model averaging and recursive model averaging, the intraday price function features extracted based on the B-spline expansion method can be used to the greatest extent to fit the residuals predicted by the ARIMA model, thereby improving the prediction accuracy of ARIMA. In addition, since the regression prediction method in the model averaging stage is model-free, when the nonlinear regression model is selected, it can capture the linear and nonlinear information of the intraday price function characteristics. Specifically, this paper uses CART as the base model, and the actual data analysis results show that BMA-ARIMA can improve the prediction accuracy of the ARIMA model and has certain robustness. Finally, the method can theoretically be extended to time series forecasting in the fields of medicine, water conservancy, electric power and environmental science.
ARIMA; B-spline basis expansion; Model average; Auxiliary information
Minsong Gao, Chuyu Feng. An improved ARIMA Stock Price Forecasting Method Based on B-spline Expansion and Model Averaging. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 10: 14-20. https://doi.org/10.25236/AJCIS.2022.051003.
[1] Liu Song & Zhang Shuai. (2021). Empirical research on stock price forecasting using ARIMA model. Economic Research Guide (25), 76-78.
[2] Wu Yuxia & Wen Xin. (2016). Short-term stock price forecast based on ARIMA model. Statistics and Decision (23), 83-86. doi: 10.13546/j.cnki.tjyjc.2016.23.051.
[3] Dong Bolun & Xu Dongyu. (2015). Prediction and Analysis of Agricultural Products Stock Prices Based on ARIMA Model. Modern Business (03), 186-188. doi: 10.14097/j.cnki.5392/2015.03.100.
[4] Bai Yujie. (2011). Application of improved time series model in rainfall forecasting. Computer Simulation (10), 141-145.
[5] Pan Difu, Liu Hui & Li Yanfei. (2008). An improved algorithm for short-term multi-step forecasting of wind speed in wind farms. Chinese Journal of Electrical Engineering (26), 87-91.
[6] Fu Xinzhong, Feng Lihua & Chen Wenchen. (2009). Application of ARIMA and ANN combined prediction model in medium and long-term runoff forecasting. Chinese Journal of Water Resources and Water Engineering (05), 105-109.
[7] Xu Jiwei & Yang Yun. (2018). Ensemble learning method: A review of research. Journal of Yunnan University (Natural Science Edition) (06), 1082-1092.