Back-propagation neural network and ARIMA algorithm for price trend analysis

Qichang Dong; Ziqi Yuan; Jinghang Guo

doi:10.1117/12.2653166

20 October 2022 Back-propagation neural network and ARIMA algorithm for price trend analysis

Qichang Dong, Ziqi Yuan, Jinghang Guo

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123502C (2022) https://doi.org/10.1117/12.2653166
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

Forecasting models have a high value in the field of finance, we can better gain in investment and it provide a better basis for the national macroeconomic control strategy. In this paper we build three forecasting models based on a combination of linear ARIMA time series and nonlinear Back-Propagation neural networks to improve the accuracy of forecasting. The first model uses direct summation; the second uses ARIMA and Back-Propagation neural network forecasting results as independent variables and actual prices as dependent variables to build a multiple linear regression model; the third uses bp neural network to indirectly compensate for the residuals of ARIMA results. The more representative gold in the international market was selected as the forecasting object, and the final errors were respectively, proving that the new combination method can improve the accuracy.

Citation Download Citation

Qichang Dong, Ziqi Yuan, and Jinghang Guo "Back-propagation neural network and ARIMA algorithm for price trend analysis", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123502C (20 October 2022); https://doi.org/10.1117/12.2653166

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