  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.
