Abstract
Cryptocurrencies, which occupy a risky position among investment instruments, continue their technological developments day by day with the speed of money transfers and the confidence in the decentralization of production. In this paper, we propose a denoised Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) method for predicting cryptocurrency prices. The daily cryptocurrency price data of Bitcoin (BTC) is collected from a freely available website (cryptocompare.com). For the prediction of BTC/USD price, we considered the average values of daily opening, high, low and closing price as OHLC value. The data set is denoised from white noise using the discrete wavelet transform method (DWT) by VisuShrink thresholding on Daubechies (db4) wavelet at level=5. OHLC and denoised OHLC (DOHLC) values are predicted using ARIMA(p,d,q) and LSTM methods. LSTM hyperparameters are evaluated using 28 different combinations. A pair of Adam-linear optimization and activation function is the best hyperparameter with the lowest mean loss value of 1.42e-03. Finally, DLSTM was found to be the best prediction method according to the Root Mean Squared Error (RMSE) prediction metric: 556.85.