Machine Learning Models for Stock Prediction Using Real-Time Streaming Data

2020 ◽  
pp. 101-108
Author(s):  
Monalisa Jena ◽  
Ranjan Kumar Behera ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Streaming Data ◽  
Learning Models ◽  
Stock Prediction ◽  
Machine Learning Models

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

2020 ◽  
Vol 26 (9) ◽  
pp. 1128-1147
Author(s):  
Ranjan Behera ◽  
Sushree Das ◽  
Santanu Rath ◽  
Sanjay Misra ◽  
Robertas Damasevicius
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Historical Data ◽  
Streaming Data ◽  
Support Vector ◽  
Learning Models ◽  
Stock Prediction ◽  
The Real ◽  
Lambda Architecture ◽  
Machine Learning Models

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.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

scholarly journals Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

2020 ◽  
Vol 143 ◽  
pp. 113083 ◽  
Author(s):  
Oscar J. Pellicer-Valero ◽  
María José Rupérez ◽  
Sandra Martínez-Sanchis ◽  
José D. Martín-Guerrero
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
Biomechanical Modeling ◽  
Learning Models ◽  
Machine Learning Models ◽  
Element Method

scholarly journals Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

APL Materials ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 053213 ◽  
Author(s):  
Michael W. Gaultois ◽  
Anton O. Oliynyk ◽  
Arthur Mar ◽  
Taylor D. Sparks ◽  
Gregory J. Mulholland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Thermoelectric Materials ◽  
Learning Models ◽  
Web Based ◽  
Materials Properties ◽  
Machine Learning Models
Sign in / Sign up

Export Citation Format

Share Document