Abstract:
Cryptocurrency is a type of currency available only in digital form, not in physical (such as
banknote and coins). There are many uses of cryptocurrencies. The most well-known benefit is their
ability to send and receive payments at a low cost and at a high speed. Cryptocurrencies, such as
bitcoin, act as a censorship-resistant alternative store of wealth that only the individual with the
privet keys to the wallet has access to. Hence, no personal bitcoin wallet can ever be frozen by the
authorities. Likewise, it is now possible to travel the world by spending cryptocurrencies. As this is
an international business all cryptocurrency transactions are done by using US dollars and the
currency market is a typical area that presents time-series data and many researchers study on it
and proposed various models. Many time-series prediction algorithms have shown their
electiveness in practice. The most common algorithms now are based on Recurrent Neural
Networks (RNN), as well as its special type Long-Short-Term- Memory (LSTM) and Gated
Recurrent Unit (GRU). The aim of this study is to forecast and compare cryptocurrencies such as
Bitcoin, Ethereum, Litecoin and Ripple and also real currencies such as Australian dollars, Euro all
against US dollars using recurrent neural network.
