An Artificial Neural Networks Based Ensemble System to Forecast Bitcoin Daily Trading Volume
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cryptocurrencies are digital assets gaining popularity and generating huge transactions on electronic platforms. We develop an ensemble predictive system based on artificial neural networks to forecast Bitcoin daily trading volume level. Indeed, although ensemble forecasts are increasingly employed in various forecasting tasks, developing an intelligent predictive system for Bitcoin trading volume based on ensemble forecasts has not been addressed yet. Ensemble Bitcoin trading volume are forecasted using two specific artificial neural networks; namely, radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN). They are adopted to respectively capture local and general patterns in Bitcoin trading volume data. Finally, the feedforward artificial neural network (FFNN) is implemented to generate Bitcoin final trading volume after having aggregated the forecasts from RBFNN and GRNN. In this regard, FFNN is executed to merge local and global forecasts in a nonlinear framework. Overall, our proposed ensemble predictive system reduced the forecasting errors by 18.81% and 62.86% when compared to its components RBFNN and GRNN, respectively. In addition, the ensemble system reduced the forecasting error by 90.49% when compared to a single FFNN used as a basic reference model. Thus, the empirical outcomes show that our proposed ensemble predictive model allows achieving an improvement in terms of forecasting. Regarding the practical results of this work, while being fast, applying the artificial neural networks to develop an ensemble predictive system to forecast Bitcoin daily trading volume is recommended to apply for addressing simultaneously local and global patterns used to characterize Bitcoin trading data. We conclude that the proposed artificial neural networks ensemble forecasting model is easy to implement and efficient for Bitcoin daily volume forecasting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it