Artificial Intelligent Systems and Machine Learning

Harshita L. Patel ¹, Amit P. Ganatra ², C. K. Bhensdadia ¹, Y. P. Kosta ³

Affiliations
1 Computer Department, Dharamsinh Desai University, Nadiad, Gujarat, IN
2 Computer Department, Charusat University, Changa, Gujarat, IN
3 CHARUSAT University, Gujarat, IN

Abstract

At present, an active research topic is the use of ensembles of classifiers. They are obtained by generating and combining base classifiers, constructed using other machine learning methods. It is an obvious approach to making decisions more reliable to combine the output of different models. Several machine learning techniques do this by learning an ensemble of models and using them in combination:prominent among these are schemes called bagging, boosting, and stacking. They can all, more often than not, increase predictive performance over a single model. And they are general techniques that can be applied to numeric prediction problems and to classification tasks. Bagging, boosting, and stacking have only been developed over the past decade, and their performance is often astonishingly good. In this paper, we do a comparative study of Boosting algorithms and also their performance comparison with Bagging and Stacking. ML algorithms implemented in WEKA (Waikato Environment for Knowledge Analysis) are used for comparative study. Results obtained over different datasets by different algorithms are compared.

Keywords

Bagging, Bayesian Network, Boosting, Classifiers, Ensemble Learning, Feature Selection, Machine learning, MLP (Multi Layer Perceptron), Naive Bayes Classifier, Predictive Accuracy, SMO (Sequential Minimal Optimization Algorithm for Training a Support Vector Classifier), and Stacking.

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Amit Ganatra ¹, Y. P. Kosta ¹

Affiliations
1 Patel Department of Computer Engineering, Charotar University of Science & Technology, CHARUSAT, Gujarat, IN

Abstract

Stock prediction is, so far, one of the popular topics not only for research purposes but also for commercial applications. Owing to its importance, a well-established school of concepts and techniques, including fundamental and technical analysis, has developed in recent decades. However, because these techniques or tools are based on totally different analytical approaches, they often yield contradictory results. More importantly, these analytical tools are heavily dependent on human expertise and justification in areas such as the location of reversal (or continuation) patterns, market patterns, and trend prediction. Predicting stock data with traditional time series analysis has proven to be difficult. An artificial neural network may be more suitable for the task primarily because no assumption about a suitable mathematical model has to be made prior to forecasting. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Our focus of study is to build neural network for stock market prediction. We propose to study feed forward back propagation network and their predictive accuracy. We propose to study architecture model of neural network and its different network parameters. The study attempts to understand network parameter like momentum, learning rate, number of neurons etc. We will compare architecture and result of above models. Our aim is to build best model by studying various parameters of the neural network. And also study other related model to compare accuracy of the model. In this study we have used R tool to implement the neural network. We have taken closing price, turnover, global indices, interest rate, and inflation as a neural network input. We proposed to include other indicator like news, currency rate, and crude price as input to the neural network. We compared stock prediction accuracy by setting different network parameters. Subsequently, an attempt is made to build and evaluate a neural network with different network parameters.  Technical as well as fundamental data are used as input to the network. In benchmark comparisons, the price prediction proves to be successful.

Keywords

Classification, Neural Network, Feature Selection, Prediction, Stock Market.

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