  This paper studies how to forecast daily closing price series of Bitcoin,
using data on prices and volumes of prior days. Bitcoin price behaviour is
still largely unexplored, presenting new opportunities. We compared our results
with two modern works on Bitcoin prices forecasting and with a well-known
recent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ
closing prices spanning a 3-year interval. We followed different approaches in
parallel, implementing both statistical techniques and machine learning
algorithms. The SLR model for univariate series forecast uses only closing
prices, whereas the MLR model for multivariate series uses both price and
volume data. We applied the ADF -Test to these series, which resulted to be
indistinguishable from a random walk. We also used two artificial neural
networks: MLP and LSTM. We then partitioned the dataset into shorter sequences,
representing different price regimes, obtaining best result using more than one
previous price, thus confirming our regime hypothesis. All the models were
evaluated in terms of MAPE and relativeRMSE. They performed well, and were
overall better than those obtained in the benchmarks. Based on the results, it
was possible to demonstrate the efficacy of the proposed methodology and its
contribution to the state-of-the-art.
