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

No Thumbnail Available

Date

2020

Authors

Das, Sushree
Rath, Santanu Kumar
Misra, Sanjay
Damasevicius, Robertas

Journal Title

Journal ISSN

Volume Title

Publisher

Graz Univ Technolgoy, inst information Systems Computer Media-iicm

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Organizational Unit
Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

Journal Issue

Abstract

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 flow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading floors 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.

Description

Misra, Sanjay/0000-0002-3556-9331; BEHERA, RANJAN KUMAR/0000-0001-9267-3621

Keywords

Spark Streaming, NodeJS, Twitter API, Lambda Architecture, MLlib

Turkish CoHE Thesis Center URL

Fields of Science

Citation

12

WoS Q

Q4

Scopus Q

Q3

Source

Volume

26

Issue

9

Start Page

1128

End Page

1147

Collections