**4. Results and Discussion**

This section consists of three parts. In the first part, a multilinear regression model is built for the BTC price prediction problem on monthly BTC prices from 18 August 2010 to 17 September 2018. Data includes macroeconomic and blockchain information indicators. The second part presents two comparative approaches: feature-based and category-based comparative analysis consisting of OLS, Ensemble methods, SVR, and MLP for the BTC price prediction problem on a daily data set from 11 October 2016 to 12 June 2017. Data is composed of macroeconomic, microeconomic, and technical indicators. All predictions in this part are out-of-fold predictions.

During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are called out-of-fold predictions, a type of out-of-sample predictions. Another analysis similar to the second part is described in the third part on different BTC datasets, including macroeconomic, microeconomic, blockchain information, and technical indicators from 1 January 2018 to 5 June 2018. For validation of results in this research, three metrics, namely RMSE, R2, and Pearson *r*, have been used to compare the out-of-sample and out-of-fold predictive models under the *T*-test at the significance level of 0.05. The *k*-fold cross-validation with *k* = 10 (so-called cross-validation on a rolling basis) is used to construct a high-performance model and have robust results. Results are averaged on 100 prediction trials.
