利用归一化改进PSO和DE的准确性：在股票价格预测中的应用
Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction
摘要：
数据采掘自20世纪80年代以来被积极地应用于股票市场。它已被用于预测股票价格，股票指数，投资组合管理，趋势检测和开发推荐系统。已经用于相同的各种算法包括人工神经网络，支持向量机，ARIMA，GARCH等不同的混合模型已经开发了这些算法与其他算法，如粗糙，模糊逻辑，GA，PSO，DE，ACO等相结合，以提高效率。本文提出了一种股票价格预测的DE-SVM模型（差分进化支持向量机）。DE已被用来选择最佳的自由参数组合的支持向量机，以改善结果。将预测结果与单独使用SVM和PSO-SVM模型（粒子群优化）的预测结果进行了比较。还研究了数据归一化对预测精度的影响。
英文摘要：
Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid models have been developed by combining these algorithms with other algorithms like roughest, fuzzy logic, GA, PSO, DE, ACO etc. to improve the efficiency. This paper proposes DE-SVM model (Differential EvolutionSupport vector Machine) for stock price prediction. DE has been used to select best free parameters combination for SVM to improve results. The paper also compares the results of prediction with the outputs of SVM alone and PSO-SVM model (Particle Swarm Optimization). The effect of normalization of data on the accuracy of prediction has also been studied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
