股票价格预测的LSSVM-ABC算法
LSSVM-ABC Algorithm for Stock Price prediction
摘要：
本文从蜜蜂群体的行为出发，提出了人工蜂群算法。ABC是一种基于随机种群的进化算法。ABC算法被认为是最新的群体智能技术之一，被提出来优化最小二乘支持向量机（LSSVM），以预测每日的股票价格。该模型是在对股票历史数据、技术指标进行研究的基础上，采用ABC算法对最小二乘支持向量机进行优化。ABC选择最佳的自由参数组合进行LSSVM，以避免过拟合和局部极小值问题，提高预测精度。采用粒子群优化算法、LSSVM和人工神经网络技术优化的LSSVM与所提出的模型进行了比较。提出的模型测试与20个数据集代表不同行业的标准普尔500股票市场。结果表明，该模型具有较快的收敛速度，在大多数情况下，其精度也优于同类技术。
英文摘要：
In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.
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。
