Monitoring the Sequence Recovery in Bitcoin Using Convolutional Neural Network and Long Short-Term Model to Hybrid Model
Keywords:
BITCOIN, Convolutional Neural Network, Hybrid Model, Knowledge Discovery, Long Short-term Model, Sequence RecoveryAbstract
This study attempts to evaluate the prediction performance of a hybrid short-term memory network (LSTM) and convolutional neural network (CNN) model in terms of the US dollar relative to Bitcoin. Since bitcoin is a pseudonymous currency, the money is associated with bitcoin addresses as opposed to actual individuals or companies. Although the proprietors of the bitcoin address are kept anonymous, every transaction on the block chain are accessible to the general world. Bit-fine provides Pycurl with up-to-date pricing information. LSTM model is implemented using Keras and Tensor Flow. As a Money Service Business (MSB), or registration, the US Financial Crimes Enforcement Network (FinCEN) classifies US bitcoin miners who have violated the regulatory guidelines for decentralized virtual currencies.
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