Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data

Journal of Universal Computer Science

Sanjay Misra¹  Ranjan Behera²  Sushree Das²  Santanu Rath²  Robertas Damasevicius³ 

[1] Covenant University;National Institute of Technology Rourkela;Vytautas Magnus University;

关键词: Spark Streaming;    NodeJS;    Twitter API;    Lambda Archi;   

DOI  :  10.3897/jucs.2020.059

来源: DOAJ

【 摘 要 】

Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

【 授权许可】

Unknown