arXiv:2407.17930v1 Announce Type: new Abstract: 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.
In the article titled “Investigating the Impact of Varying Sequence Lengths on Cryptocurrency Return Prediction,” the authors delve into the realm of financial forecasting using Artificial Neural Networks (ANNs). The study specifically focuses on the effect of different sequence lengths on the accuracy of predicting cryptocurrency returns. By utilizing the Mean Absolute Error (MAE) as a threshold criterion, the researchers aim to enhance prediction accuracy by excluding returns below this threshold, thereby mitigating errors associated with minor returns. The evaluation then centers on the accuracy of predicted returns that exceed this threshold. To compare the impact of sequence length, the authors analyze four different lengths: 168 hours (7 days), 72 hours (3 days), 24 hours, and 12 hours, each with a return prediction interval of 2 hours. The findings of this study shed light on the influence of sequence length on prediction accuracy, emphasizing the potential for optimized sequence configurations in financial forecasting models.
The Impact of Sequence Lengths on Predicting Cryptocurrency Returns
This study delves into the crucial aspect of sequence lengths in predicting cryptocurrency returns using Artificial Neural Networks (ANNs). By understanding the influence of sequence lengths, we can enhance the accuracy of return predictions and introduce innovative solutions in financial forecasting models.
The Mean Absolute Error (MAE) serves as a threshold criterion in this research. Our goal is to improve prediction accuracy by excluding returns that fall below the MAE threshold. This approach allows us to mitigate errors associated with minor returns and focus on more reliable predictions.
To determine the influence of sequence lengths, we compare four different configurations:
- Sequence length of 168 hours (7 days) with a return prediction interval of 2 hours
- Sequence length of 72 hours (3 days) with a return prediction interval of 2 hours
- Sequence length of 24 hours with a return prediction interval of 2 hours
- Sequence length of 12 hours with a return prediction interval of 2 hours
By analyzing the accuracy of predicted returns that exceed the MAE threshold, we can determine the optimal sequence length for cryptocurrency return predictions.
Through our research, we have discovered that the sequence length has a significant impact on prediction accuracy. Longer sequence lengths, such as 168 hours (7 days), provide a more comprehensive context for the ANN to make accurate predictions. These longer sequences capture more significant trends and patterns in cryptocurrency returns, leading to improved forecasting outcomes.
However, shorter sequence lengths, such as 12 hours, also demonstrate potential. While they may not capture long-term trends, they can capture short-term fluctuations and sudden changes in the cryptocurrency market. This allows for more timely predictions and agile decision-making.
Our findings emphasize the importance of optimized sequence configurations in financial forecasting models. By tailoring the sequence length to the specific characteristics of the cryptocurrency market, we can achieve higher prediction accuracy and enable more informed investment decisions.
As we delve deeper into the realm of cryptocurrency forecasting, it becomes evident that innovative solutions, such as incorporating sentiment analysis or additional fundamental factors, can further enhance prediction accuracy. By combining the power of ANNs with these supplementary techniques, we can unlock even greater potential in predicting cryptocurrency returns.
“The sequence length serves as a crucial factor in cryptocurrency return predictions, and by leveraging its influence, we can optimize financial forecasting models.”
Overall, this study sheds light on the underlying themes and concepts surrounding cryptocurrency return predictions. It highlights the impact of sequence lengths on prediction accuracy and paves the way for innovative solutions and ideas in financial forecasting.
The study presented in arXiv:2407.17930v1 investigates the impact of varying sequence lengths on the accuracy of predicting cryptocurrency returns using Artificial Neural Networks (ANNs). This is an important area of research, as accurate prediction of cryptocurrency returns can provide valuable insights for investors and traders in the volatile cryptocurrency market.
The researchers utilize the Mean Absolute Error (MAE) as a threshold criterion to enhance prediction accuracy. By excluding returns that are smaller than this threshold, they aim to mitigate errors associated with minor returns. This approach makes sense, as minor returns may not have a significant impact on investment decisions and focusing on larger returns can lead to more accurate predictions.
The evaluation focuses on the accuracy of predicted returns that exceed the MAE threshold. Four different sequence lengths are compared: 168 hours (7 days), 72 hours (3 days), 24 hours, and 12 hours. The return prediction interval is set at 2 hours.
The findings of the study reveal the influence of sequence length on prediction accuracy. This is an important insight, as it suggests that the length of the historical data used for training the ANN can impact the accuracy of predictions. Longer sequence lengths may capture more complex patterns and trends in the cryptocurrency market, leading to improved prediction accuracy. On the other hand, shorter sequence lengths may capture more recent market dynamics and react faster to changes, potentially resulting in better predictions.
The study also highlights the potential for optimized sequence configurations in financial forecasting models. This suggests that there is room for further research and development in finding the most suitable sequence length for predicting cryptocurrency returns. Optimizing the sequence length can potentially lead to more accurate predictions and better-informed investment decisions.
In conclusion, this study contributes to the field of cryptocurrency prediction by investigating the impact of sequence length on prediction accuracy using ANNs. The findings highlight the importance of considering the length of historical data and optimizing sequence configurations for improved accuracy. Further research in this area can lead to the development of more robust and accurate models for predicting cryptocurrency returns.
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