HyBiLSTM: Multivariate Bitcoin Price Forecasting Using Hybrid Time-Series Models With Bidirectional LSTM-Reference-Cited by-同舟云学术

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HyBiLSTM: Multivariate Bitcoin Price Forecasting Using Hybrid Time-Series Models With Bidirectional LSTM

Published:2024 Issue: Volume:12 Page:50792-50808
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mardjo Anny¹^ORCID,Choksuchat Chidchanok²^ORCID

Affiliation:

1. College of Digital Science, Prince of Songkla University, Hat Yai, Thailand

2. Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Thailand

Funder

Digital Science for Economy, Society, Human Resources Innovative Development and Environment

Reinventing Universities & Research Institutes

Ministry of Higher Education, Science, Research and Innovation, Thailand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10494511.pdf?arnumber=10494511

Reference47 articles.

1. Robust Speech Emotion Recognition Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm

2. Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants

3. A Gated Recurrent Unit Approach to Bitcoin Price Prediction

4. Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

5. Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

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