基于CART与DBSCAN相结合的股票预测模型研究

Yibu Ma
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引用次数: 5

摘要

随着世界股票市场电子化和智能化的发展,股票数据的积累随着时间的推移越来越大。如何在海量数据中发现信息的隐藏规律是一个备受关注的问题。在此背景下,本文探索了将决策树算法与聚类算法相结合的数据挖掘方法。此外,本文结合CART算法和DBSCAN算法完成了股票预测,通过大量的实验进行参数检验,建立了适用性较好的预测模型。通过以上工作,该预测模型具有较高的准确性,为投资决策提供了科学的理论支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Research of Stock Predictive Model Based on the Combination of CART and DBSCAN
Along with the development of electronic and intelligence in the world's stock market advances, the accumulation of the stock data grows larger over time. It is of great concern on the ways to find the hidden rules of information in the mass of data. Given the background above, this paper explores the methods of data mining by using the combination of Decision tree algorithm and Clustering algorithm. In addition, this paper accomplishes stock forecasting by combining CART algorithm and DBSCAN algorithm to build a predictive model with good applicability through a large number of experiments for parameter testing. According to the works above, the predictive model has a high accuracy and provides a scientific theory supporting the investment decisions.
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