Use of Artificial Intelligence in Ethereum Forecasting: The Deep Learning Models RNN and CNN with Ensemble Averaging Technique

FOZIA ZEESHAN; Narayan Nepal; Mohammad Norouzifard

Authors

FOZIA ZEESHAN Lincoln University New Zealand
Narayan Nepal Head of Faculty, Yoobee College of Creative Innovation Christchurch, New Zealand
Mohammad Norouzifard 3Auckland Bioengineering Institute, University of Auckland. Yoobee College of Creative Innovation. Auckland, New Zealand

Keywords:

LSTM (Long short-term memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural network, Ensemble learning, Deep learning

Abstract

In the fast-evolving cryptocurrency market, accurately predicting Ethereum prices is crucial for investors, traders, and financial analysts. Traditional machine learning (ML) models often struggle to capture the market's complex dynamics due to their inability to consider all influencing factors. This study introduces an advanced ensemble machine learning approach to enhance Ethereum price prediction accuracy. By combining the strengths of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) models, our ensemble averaging method compensates for individual model weaknesses, improving forecast reliability and precision. Results show that our ensemble model offers significant advantages, particularly in terms of generalizability and resistance to overfitting with LSTM and CNN models and this technique is offering a more effective tool for navigating cryptocurrency market complexities. This research highlights the importance of ensemble learning in financial forecasting and provides a practical framework for developing superior predictive models. “Moreover, This study explores an advanced ensemble machine learning approach to enhance Ethereum price predictions, combining the strengths of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) models. While Bi-LSTM individually exhibits slightly higher performance in our tests, the ensemble method demonstrates enhanced stability and reliability, making it a valuable tool for navigating the unpredictable dynamics of the cryptocurrency market. We found that Bi-LSTM is good on its own, but the balanced approach of the ensemble model is far better, especially when it comes to generalizability and overfitting resistance. Insights into creating flexible and trustworthy prediction models are provided by this study, which highlights the possibilities of ensemble learning in financial forecasting.

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