Abstract: Cryptocurrencies are new economic and financial tools with special and innovative features. Investment in cryptocurrency has been in trend from last many years. Bitcoin is one of the most popular and valuable cryptocurrency. Many studies have been done on bitcoin price prediction using various parameters which includes bitcoin factors, social media etc. The most important thing is that they are not related assets, not issued by any government or central authority. In recent years, cryptocurrency (Bitcoin) is rising and become an attractive investment for traders. Unlike stocks or foreign exchange, Bitcoin price is fluctuated, mainly because of its 24- hours a day trading time without close time. To minimize the risk involved and maximize capital gain, traders and investors need a way to predict the Bitcoin price trend accurately. However, many previous works on cryptocurrency price prediction forecast short-term Bitcoin price, have low accuracy and have not been cross-validated. A comparative study of the various parameters affecting bitcoin price prediction is done based on Root Mean Square Error using various deep learning models like Multilayer Perceptron , Long Short Term Memory.
Keywords:
LSTM Long Short Term Memory
MLP Multilayer Percepton
RSME Root Mean Square Error
MSE Mean Squared Error
RNN Recurrrent Neural Network
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DOI:
10.17148/IJARCCE.2021.10744
