GA-PAT-KNN:时间序列预测框架

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI:10.1109/IJCNN.2011.6033524

Armando A. Gonçalves, Igor Alencar, Ing Ren Tsang, George D. C. Cavalcanti

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引用次数: 0

摘要

提出了一种结合遗传算法(GA)、部分轴搜索树(PAT)和k近邻算法(KNN)的时间序列预测框架。这种方法是基于从股票的技术分析中获得的信息。实验表明，GAs可以捕获最相关的变量，并提高预测股票价格指数每日变化方向的准确性。与其他模型的比较表明了该框架的优越性

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GA-PAT-KNN: Framework for time series forecasting

A novel framework for time series prediction that integrates Genetic Algorithm (GA), Partial Axis Search Tree (PAT) and K-Nearest Neighbors (KNN) is proposed. This methodology is based on the information obtained from Technical analysis of a stock. Experiments have shown that GAs can capture the most relevant variables and improve the accuracy of predicting the direction of daily change in a stock price index. A comparison with other models shows the advantage of the proposed framework

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The 2011 International Joint Conference on Neural Networks

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