Time series forecasting using evolutionary neural nets implemented in a volunteer computing system

Q1 Economics, Econometrics and Finance

Intelligent Systems in Accounting, Finance and Management Pub Date : 2017-08-08 DOI:10.1002/isaf.1409

V.M. Rivas, E. Parras-Gutiérrez, J.J. Merelo, M.G. Arenas, P. García-Fernández

引用次数: 8

Abstract

jsEvRBF is a time-series forecasting method based on genetic algorithm and neural nets. Written in JavaScript language, can be executed in most web browsers. Consequently, everybody can participate in the experiments, and scientists can take advantage of nowadays available browsers and devices as computation environments. This is also a great challenge as the language support and performance varies from one browser to another. In this paper, jsEvRBF has been tested in a volunteer computing experiment, and also in a single-browser one. Both experiments are related to forecasting currencies exchange, and the results show the viability of the proposal.

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在志愿者计算系统中实现的进化神经网络时间序列预测

jsEvRBF是一种基于遗传算法和神经网络的时间序列预测方法。用JavaScript语言编写，可在大多数web浏览器中执行。因此，每个人都可以参与实验，科学家可以利用当今可用的浏览器和设备作为计算环境。这也是一个巨大的挑战，因为语言支持和性能因浏览器而异。在本文中，jsEvRBF已经在志愿者计算实验和单浏览器实验中进行了测试。这两个实验都与预测货币兑换有关，结果表明了该建议的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance

CiteScore

6.00

自引率

0.00%

发文量

期刊介绍： Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.