@article{MAKHILLJEAS201914817654,
    title = {Forecasting Stock Index Data Using Hybrid Models},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {8},
    pages = {2752-2763},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2752.2763},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2752.2763},
    author = {Kumar,C. and},
    keywords = {Hybrid model,Laplacian score,multi cluster,co-relation,ANFIS,Genetic algorithm,particle swarm
optimization},
    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.}
    }