Prediction of Stock Market using Ensemble Model

In the modern Digital Era, Data Mining is the powerful area for analyzing the large data sets to get unexpected relationships (models). The analysis of statistical data on sequential data points measured at regular time interval over a period of time is time series analysis. Time series analysis is used in predicting future occurrence of a time based event. One of the main areas where time series analysis is implied is in stock market prediction. The two important classification ways are Support Vector Machine (SVM) and Naïve Bayes. SVM is a method used for the foreseeing of financial time based data sets. It uses a function called risk which contains a mistake and a term. The basic principle which is used to obtain this may be called as minimization of structural risk. Naïve Bayes model assigns class labels for problem instances which can be denoted as vectors of feature measurements. For a given group variable, it takes into consideration that the numerical record of a specified feature is unique of others. Purpose of the present investigation is to develop an ensemble model namely AdaSVM and AdaNaive to analyse the stock data by comparing SVM and Naïve Bayes methods. The performance evaluation measures such as accuracy and classification error were computed individually for