Cryptocurrency Price Prediction Based on CNN-BiLSTM-AM Model

Meijun Gao

2024

Abstract

Contemporarily, as the cryptocurrency market has experienced unprecedented rapid growth, its high price volatility and complex nonlinear dynamic characteristics have made cryptocurrency price prediction a focus of concern. Accurate price prediction is crucial for investors for it helps investors effectively manage investment risks and significantly increase investment returns. This study innovatively proposes a hybrid prediction model which integrates convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM) and attention mechanism (AM), named as the CNN-BiLSTM-AM model, aiming to accurately predict the price of three typical cryptocurrencies, BTC, ETH, and LTC. CNN is used in feature extraction. BiLSTM is introduced to process time series data. AM tracks how feature states affect cryptocurrency closing prices over history. With the aim of verifying the effectiveness of the hybrid model, the next day's closing price of BTC, ETH, and LTC is selected as the test dataset for this model and three other mainstream prediction models. Experimental results indicate that this hybrid model ranks first in terms of prediction accuracy, specifically manifested in the smallest Mean Absolute Error (MAE) and Root Mean Squard Error (RMSE), along with the highest R-Square (R²). These results indicate that the hybrid CNN-BiLSTM-AM model can be adopted as a powerful tool for investors to formulate investment strategies and make actual investment decisions.

Paper Citation

in Harvard Style

Gao M. (2024). Cryptocurrency Price Prediction Based on CNN-BiLSTM-AM Model. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 516-523. DOI: 10.5220/0013269800004568

in Bibtex Style

@conference{ecai24,
author={Meijun Gao},
title={Cryptocurrency Price Prediction Based on CNN-BiLSTM-AM Model},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={516-523},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013269800004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Cryptocurrency Price Prediction Based on CNN-BiLSTM-AM Model
SN - 978-989-758-726-9
AU - Gao M.
PY - 2024
SP - 516
EP - 523
DO - 10.5220/0013269800004568
PB - SciTePress