A New Hybrid Cryptocurrency Returns Forecasting Method Based on Multiscale Decomposition and an Optimized Extreme Learning Machine Using the Sparrow Search Algorithm-Reference-Cited by-同舟云学术

A New Hybrid Cryptocurrency Returns Forecasting Method Based on Multiscale Decomposition and an Optimized Extreme Learning Machine Using the Sparrow Search Algorithm

Published:2022 Issue: Volume:10 Page:60397-60411
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Du Xiaoxu¹^ORCID,Tang Zhenpeng²^ORCID,Wu Junchuan³,Chen Kaijie¹,Cai Yi¹

Affiliation:

1. School of Economics and Management, Fuzhou University, Fuzhou, China

2. School of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China

3. School of Economics and Management, Nanchang University, Nanchang, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09785838.pdf?arnumber=9785838

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3. Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM

4. A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices

5. A new ensemble deep learning approach for exchange rates forecasting and trading

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