Enhancing Bitcoin Price Predictions with a Data Selection Approach for Linear Regression

Abstract

This research employs a Selective Neural Network Ensemble driven by Genetic Algorithms, employing an Artificial Neural Network ensemble methodology. The ensemble incorporates base model of any neural network is the multi-layered perceptron. Aim to explore the correlation between Bitcoin's features and its subsequent day's price movement. Leveraging approximately 200 cryptocurrency attributes over a two-year span, the ensemble predicts the direction of Bitcoin's price the following day, aiming to assess its practicality and relevance in real-world scenarios. In a comparative the ensemble-based trading strategy is evaluated using back-testing analysis over a 50-day period versus a “prior day trend“ trading approach. The former approach demonstrated noteworthy results, offering insights into its potential effectiveness. The best data range for training a Bitcoin price prediction model is determined by applying financial terms and methods, such as Simple Moving Average and Exponential Moving Average, as described in the article. A Linear Regression Model addresses the problem of choosing the appropriate dataset for improved forecasting results, achieving a high 97% prediction accuracy by adhering to the model's recommended data piece.