GA-PAT-KNN: Framework for time series forecasting

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

Abstract: 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.