A hybridized ELM-Jaya forecasting model for currency exchange prediction

被引：30

作者：

Das, Smruti Rekha ^{[1
,2
]}

Mishra, Debahuti ^{[1
,2
]}

Rout, Minakhi ^{[1
,2
]}

机构：

[1] Siksha Anusandhan Univ, Dept Comp Sci & Engn, Bhuabneswar, Odisha, India

[2] GNITC, Dept Informat Technol, Hyderabad, India

来源：

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES | 2020年 / 32卷 / 03期

关键词：

Currency exchange prediction; Extreme Learning Machine (ELM); Jaya; Neural Network (NN); Functional Link Artificial Neural Network (FLANN); ARTIFICIAL NEURAL-NETWORK; LEARNING-BASED OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; TLBO ALGORITHM; SYSTEM; PERFORMANCE; REGRESSION; ADVANTAGES; MACHINE;

D O I：

10.1016/j.jksuci.2017.09.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper establishes a hybridized intelligent machine learning based currency exchange forecasting model using Extreme Learning Machines (ELMs) and the Jaya optimization technique. This model can very well forecast the exchange price of USD (US Dollar) to INR (Indian Rupee) and USD to EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. The proposed ELM-Jaya model has been compared with existing optimized Neural Network and Functional Link Artificial Neural Network based predictive models. Finally, the model has been validated using various performance measures such as; MAPE, Theil's U, ARV and MAE. The comparison of different features demonstrates that the technical indicators outperform both the statistical measures and a combination of statistical measures and technical indicators in ELM-Jaya forecasting model. (C) 2017 The Authors. Production and hosting by Elsevier B.V.

引用

页码：345 / 366

页数：22

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