Comparative Analysis of Stock Price Prediction by ANN and RF Model

Abstract

The elementary goal of this paper is to predict the best model for estimation of stock market. Machine Learning is a blooming field in computer science that has contributed to many predictions and analysis-based algorithm in Financial and economical field. Some of the algorithms used for predictions are Random Forest (RF), Support vector machine (SVM), Long-Short Term Memory (LSTM), Artificial Neural Networks (ANN). Random Forest is an ensemble supervised learning algorithm for classification problems with high accuracy factor. ANN has matured to a great extend over the past years. With the advent of high-performance computing ANN has assumed tremendous significance and huge application potentials in recent years. The innovation of ANN technology mimics the large interconnections and networking that exists between the nerve cells to process complex task. The paper has presented ANN and RF model for stock price estimation based on historical data and computed the future price, with comparative result analysis of their performance. Further, a candlestick model is designed of the stock to show the variation in price of stock over a stipulated period of time. Keyword: Random Forest, Candle-stick, ANN, RNN, CNN, Support Vector Machine, Deep Learning

Prediction of Customer Churn in Telecom Industry: A Machine Learning Perspective

“…In addition to their versatility, adaptability, and scalability, ANNs are also suitable for handling large datasets and highly complex Machine Learning problems, like image classification, speech recognition, or video recommendation. In ANN algorithms, the aim is to create the most minimal error function possible by selecting the optimal weights and bias terms [12]. thought of as the most sophisticated version of Machine Learning.…”

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“…In addition to their versatility, adaptability, and scalability, ANNs are also suitable for handling large datasets and highly complex Machine Learning problems, like image classification, speech recognition, or video recommendation. In ANN algorithms, the aim is to create the most minimal error function possible by selecting the optimal weights and bias terms [12]. thought of as the most sophisticated version of Machine Learning.…”