Prediction of Stock Market using Ensemble Model

Abstract

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 the stock market data set. The experimental result shows AdaSVM and AdaNaive is acceptable than SVM and Naïve Bayes.

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

“…They have also mentioned that change of technique and increase of input data may provide even more accurate result. Finally, they have agreed that continuous research is essential in developing an acceptable stock forecasting method [15].…”

Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression

“…Asad [3] and Weng et al [7] has used random forest classifiers in ensembles for predicting the stock. Narayanan and Govindarajan [1] has used Naïve Bayes' algorithm and SVMs to build ensembles for stock prediction. Patel et al [10] have conducted a comparison between the SVMs, Naive Bayes, Artificial Neural Networks (ANNs), and Random Forests.…”

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

“…To do that, the models have to be developed by providing it with different algorithms and with a training dataset to learn and thus the output will be given based on probability distribution function and frequency of the dataset [21]. In Rapidminer platform for getting unexpected relationships from large dataset, data mining stands as one of the most powerful field of study [28].…”