An Artificial Neural Networks Based Ensemble System to Forecast Bitcoin Daily Trading Volume
| Autor: | Raafat George Saadé, Salim Lahmiri, Fassil Nebebe, Danielle Morin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cryptocurrency
021103 operations research Artificial neural network Ensemble forecasting Computer science business.industry Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies Volume (computing) Feed forward Cloud computing 02 engineering and technology Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Feedforward neural network 020201 artificial intelligence & image processing Artificial intelligence business Reference model computer Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Cloudtech |
| DOI: | 10.1109/cloudtech49835.2020.9365913 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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