Corporate information system for exchange rate analysis and commodity money forecasting

Zhengbing Hu; Dmytro Uhryn; Yurii Ushenko; Oksana Yatsko; Oleksandr Kodrianu; Myroslav Kovalchuk; Yurii Tomka

doi:10.1117/12.3009679

5 January 2024 Corporate information system for exchange rate analysis and commodity money forecasting

Zhengbing Hu, Dmytro Uhryn, Yurii Ushenko, Oksana Yatsko, Oleksandr Kodrianu, Myroslav Kovalchuk, Yurii Tomka

Proceedings Volume 12938, Sixteenth International Conference on Correlation Optics; 129380N (2024) https://doi.org/10.1117/12.3009679
Event: International Conference Correlation Optics (COR 2023), 2023, Chernivtsi, Ukraine

Abstract

An intelligent web portal for analysing and forecasting the exchange rate of commodity money has been developed. The result of this research is a comprehensive system that allows for the analysis of the commodity money market, including price forecasting and risk assessment. The study identified the prophet method for predicting the exchange rate of a commodity unit and the monte carlo method for predicting the value of an investment portfolio with various assets. These methods allow us to reliably determine future trends based on the analysis of available data. To implement the web portal, we used the streamlit framework, which allows us to quickly develop interactive web applications using machine learning and data analysis tools. The scikit-learn library was used to work with machine learning, which has a wide range of tools for applying classical machine learning methods. The yfinance library was also used to retrieve financial data from the Yahoo Finance web service.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengbing Hu, Dmytro Uhryn, Yurii Ushenko, Oksana Yatsko, Oleksandr Kodrianu, Myroslav Kovalchuk, and Yurii Tomka "Corporate information system for exchange rate analysis and commodity money forecasting", Proc. SPIE 12938, Sixteenth International Conference on Correlation Optics, 129380N (5 January 2024); https://doi.org/10.1117/12.3009679

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
4 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Data analysis

Performance modeling

Education and training

Machine learning

Monte Carlo methods

Process modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years