Kumar  Vasimalla, C.  Narasimham, Deekshitha  , 
    Forecasting Stock Index Data Using Hybrid Models,
    Journal of Engineering and Applied Sciences,
    Volume 14,Issue 8,
    2019,
    Pages 2752-2763,
    ISSN 1816-949x,
    jeasci.2019.2752.2763,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2019.2752.2763)
    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.
    Keywords: Hybrid model;Laplacian score;multi cluster;co-relation;ANFIS;Genetic algorithm;particle swarm
optimization