@article {10.3844/jcssp.2017.422.429,
article_type = {journal},
title = {Integrated Multiple Linear Regression-One Rule Classification Model for the Prediction of Stock Price Trend},
author = {Han, Lock Siew and Nordin, Md Jan},
volume = {13},
number = {9},
year = {2017},
month = {May},
pages = {422-429},
doi = {10.3844/jcssp.2017.422.429},
url = {https://thescipub.com/abstract/jcssp.2017.422.429},
abstract = {One of the main problems of predicting stock price with regression approach is overfitting a model. An overfit model becomes tailored to fit the random noise in the dataset rather than reflecting the overall population. For this it is necessary to construct an integrated regression-classification model to approximate the true model for the entire population in the dataset. The proposed model integrates Multiple Linear Regression algorithm and One Rule (OneR) classification algorithm. Initially the prediction was treated with regression approach where the outputs were in numerical values. After that a classification model was used to interpret the regression outputs and then classified the outcomes into Profit and Loss class labels. The test results were compared to those obtained with standard classification algorithms which included OneR, Zero Rule (ZeroR), Decision Tree and REP Tree. The results showed that the regression-classification model were significantly more successful than the standard classification algorithms.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}