Journal of Clinical and Molecular Endocrinology

Abstract

Designing a system for analytics of high-frequency data (big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposesthe processing and analytics ofstochastic high-frequency stock market data using a modified version of suitable gradient boosting machine (GBM). The experimental results obtained are compared with deep learning and auto-regressive integrated moving average (ARIMA) methods. The results obtained using modified GBM achieve the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.