Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Forecasting is an important and widely popular topic in the research of system modeling. In this study, we proposed a six 2-stage hybrid prediction models, wherein Laplacian Score (LS), Multi Cluster based Feature Selection (MCFS), Correlation Based feature Selection (CBS) is used to construct Stage-1, followed by invoking Adaptive Network based Fuzzy Inference System (ANFIS) trained by Genetic Algorithm (GA), Particle Swarm Optimization (PSO)(Stage-2). We tested our model with Hang Seng Index (HSI) data and TAIEX stock market transaction data from 1998-2006. The results compared with the existing models in the literature, the comparison shows that the proposed model LS+ANFIS+GA outperformed the listing models in terms of both of Root Mean Squared Error (RMSE) and Theil’s U statistic.
How to cite this article:
Kumar Vasimalla, C. Narasimham and Deekshitha . Forecasting Stock Index Data Using Hybrid Models.
DOI: https://doi.org/10.36478/jeasci.2019.2752.2763
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.2752.2763