Cluster Based Classification Model for Trend Prediction in Time Series Data

Abstract

Abstract: Real-time stock market trend prediction plays a essential role in the technical analysis for trend prediction process. Traditional historical technical indicators are difficult to trace and predict the trend due to noise or uncertainty in the training dataset. Since, most of the traditional homogeneous or heterogeneous stocks are used to predict the overall market sentiment for the institutions or retailors. Also, traditional approaches are difficult to find the outliers in the technical indicators data for the clustering process. These models only consider the static metrics or static outlier meaures due to variation in data distribution according to time basis. In this work, a hybrid knn classifier is used to predict the trend of the stock.