Comparison of different Artificial Neural Networks for Bitcoin price forecasting
This is an incremental improvement for cryptocurrency traders and financial analysts, focusing on optimizing sequence lengths in existing ANN methods.
This study tackled the problem of predicting Bitcoin prices using Artificial Neural Networks by investigating how varying sequence lengths affect accuracy, finding that sequence length influences prediction accuracy and suggesting potential for optimized configurations in financial forecasting.
This study investigates the impact of varying sequence lengths on the accuracy of predicting cryptocurrency returns using Artificial Neural Networks (ANNs). Utilizing the Mean Absolute Error (MAE) as a threshold criterion, we aim to enhance prediction accuracy by excluding returns that are smaller than this threshold, thus mitigating errors associated with minor returns. The subsequent evaluation focuses on the accuracy of predicted returns that exceed this threshold. We compare four sequence lengths 168 hours (7 days), 72 hours (3 days), 24 hours, and 12 hours each with a return prediction interval of 2 hours. Our findings reveal the influence of sequence length on prediction accuracy and underscore the potential for optimized sequence configurations in financial forecasting models.