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Stock Forecasting Based on Random Forest and ARIMA Models
- 1 Department of Mathematics, Hangzhou Normal University, Hangzhou, China
* Author to whom correspondence should be addressed.
Abstract
In this paper, a Random Forest model and an ARIMA model are established to predict stock closing prices. The data from six Chinese stocks—Shandong Gold Mining, WanYi Technology, iFLYTEK, Space-Time Technology, TianYue Advanced Materials Technology, and Harmontronics Intelligent Technology—spanning from January 1, 2024, to December 30, 2024, are used. The results show that these two models are more accurate in short-term stock price prediction. By combining and comparing these two models, we conclude that the Random Forest model predicts the next day's closing price highly accurately and has obvious advantages in ultra-short-term trading. This offers insights for investors and related researchers in pursuit of short-term profits.
Keywords
closing price of stocks, time series, Random Forest model, ARIMA model
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Cite this article
Cai,T. (2025). Stock Forecasting Based on Random Forest and ARIMA Models. Theoretical and Natural Science,101,117-127.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MPCS 2025 Symposium: Mastering Optimization: Strategies for Maximum Efficiency
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