[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127242-en":3,"doc-seo-127242-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127242,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model - vol. 9 - Article","Prediction models in foreign exchange markets have gained popularity through machine learning techniques, enabling improved forecasting performance under algorithm-specific criteria. Model development and deployment remain complex due to heterogeneous research approaches and the high computational cost of training. This article designs a microservices-oriented technological platform to reduce resource consumption and support integration of multiple techniques and evaluation criteria in a web environment, strengthening analysis and validation.","International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 9, Nº1  \nDesign of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model  \nK. Gordillo-Orjuela, P. A. Gaona-García*, C. E. Montenegro-Marín*  \nFaculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá (Colombia)  \n* Corresponding author: [cemontenegrom@udistrital.edu.co](cemontenegrom@udistrital.edu.co) (C. E. Montenegro-Marín), [pagaonag@udistrital.edu.co](pagaonag@udistrital.edu.co) (P. A. Gaona-García)  \nReceived 22 February 2024 | Accepted 22 October 2024 | Published 25 November 2024  \nAbstract   \nPrediction models in foreign exchange markets have been very popular in recent years, and in particular, through the use of techniques based on Machine Learning. This growth has made it possible to train several techniques that increasingly allow us to improve predictions according to the criteria that each algorithm supports and can cover. However, the development of these models and their deployment within computer platforms is a complex task, given the variety of approaches that each researcher uses based on the training process and therefore by definition of the model, which leads to the consumption of high computing resources for its training, as well as various processes for its deployment. For this reason, the following article focuses on designing a technological platform oriented to micro services, which minimizes the consumption of resources and facilitates the integration of various techniques and the analysis of various criteria, which improves their analysis and validation in a Web environment.  \nI. Introduction  \nINMraeccenhinyearsLearni,nagrt(ifiMcLi)alhianvteelgligenivencrieseantoda ilnarparticularge amountthoferaerseeaarofchon price prediction in financial markets, such as hydrocarbons, precious metals, currencies, among others [1] . Currently, a series of models and algorithms have been proposed that seek to predict the value of different currencies in the foreign exchange market, also known as \"Forex\".  \nWith the growing importance of artificial intelligence, prediction models based on machine learning have been developed, within which we can find seven broad categories: regression methods, optimization techniques, support vector machines (SVMs), neural networks, chaos theory, pattern-based methods, and other methods that include natural language processing [2].  \nDespite the development and rise of studies based on prediction techniques and models to analyze behaviors and characteristics of algorithms based on machine learning, the process of choosing, analyzing and implementing them within an application scenario is a complex process given the variety of resources, criteria and computational aspects that are required to simulate them within a work environment. Therefore, the design of a computer platform that  \nallows the comparison of machine learning techniques could be of great value and usefulness to carry out the choice of models that allow the optimization and prediction of the value of currencies within specific scenarios and, in turn, allow the combination of some of these models to obtain even more accurate results.  \nThe objective of the following article is to propose the design of a software platform that allows the execution of several models based on Machine Learning, to carry out prediction processes in the prices ofthe foreign exchange market, in order to identify elements that facilitate their implementation within various scenarios and improve the process of deployment and measurement of these models in various scenarios.  \nTherestofthe article is divided as follows: in Section IIthe background is addressed where the different methods, techniques and mechanisms that have been developed for prediction within the foreign exchange market will be analyzed, in order to identify which of them can be implemented within a computational platform. Section III pr","cbCairFby0JrmB17","https://ap.wps.com/l/cbCairFby0JrmB17","pdf",1004476,1,11,"English","en",105,"# Introduction\n## Forex Currency Study\n## Analysis Tools and A","[{\"question\":\"What problem does the article address in machine learning-based currency prediction?\",\"answer\":\"It addresses the complexity of developing and deploying prediction models within computing platforms due to diverse researcher approaches and high resource demands for training.\"},{\"question\":\"How does the proposed platform help with model development and deployment?\",\"answer\":\"It uses a microservices-oriented technological platform to minimize resource consumption and facilitate integration of different machine learning techniques for evaluation in a web environment.\"},{\"question\":\"What types of analysis are discussed for the foreign exchange (Forex) market?\",\"answer\":\"The paper distinguishes fundamental analysis and technical analysis, focusing specifically on technical analysis that often relies on statistical graphs and machine learning techniques.\"}]","Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model - 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