A machine learning approach to analyze the relationship between Bitcoin transaction behavior and currency exchange rates
|Keywords:||比特幣;自適應增強;類神經網路;匯率;支援向量迴歸;Bitcoin;Adaboost;Exchange rate forecasting;Affinity Propagation;Neural Network;Support Vector Machine|
As the most popular digital currency, Bitcoin has its own unique characteristics, such as decentralize management and blockchain ledger technology. In addition, its volatility is much greater than traditional currencies. The blockchain records every transition since the beginning of Bitcoin. From the perspective of big data analytics, Bitcoin contains rich and valuable transition information, which may help the discovery of hidden knowledge about transistors. Our research is expected to precisely predict the exchange rate of Bitcoin through six different features from transistors, three different machine learning algorithms, and various kinds of currency attributes. The experimental results show that the proposed system can effectively model the relationship between the exchange rate change and the transition behavior of Bitcoin. It also displays that traditional currencies seldom influence the change rate of Bitcoin, while Bitcoin users’ behavior is an important factor that makes tremendous difference when forecasting Bitcoin change rate. Finally, compared to artificial neural networks and support vector machines, the learning model based on Adaboost algorithm can achieve the most accurate prediction results.
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