TY - JOUR ID - 79110 TI - Crypto- Currency Price Prediction with Decision Tree Based Regressions Approach JO - Journal of Algorithms and Computation JA - JAC LA - en SN - 2476-2776 AU - naghib moayed, ali AU - Habibi, Reza AD - Department of Statistics, Allameh Tabatabyee University AD - Iran Banking Institute, Central Bank of Iran Y1 - 2020 PY - 2020 VL - 52 IS - 2 SP - 29 EP - 40 KW - Crypto currency price prediction KW - Decision Tree KW - ARIMA DO - 10.22059/jac.2020.79110 N2 - Generally, no one can reject the fact that crypto currency market is expanded rapidly during last few years as, nowadays, crypto currency market is attractive for both traders and business who are not willing to pay for FATF services for transferring money. With this in mind, crypto currency price prediction is crucial for many people and business entities. While there have been quite a few conventional statistical models to forecast crypto currency prices, we decided to make price prediction using decision Tree Based Regression. In this research we devised a decision tree models to predict Bitcoin which is the most renowned and frequently used crypto currency. we used Volume from, Volume to, New addresses, Active addresses, large transaction count, Block height, Hash rate, Difficulty, Current supply as predictor variables in addition to historical crypto currency price data during the with a total of 1000 Observations. We find that forecasting accuracy of decision tree models are higher than benchmark models such as linear regression and autoregressive integrated moving average(ARIMA). UR - https://jac.ut.ac.ir/article_79110.html L1 - https://jac.ut.ac.ir/article_79110_6156628d39397c2b78824976d69d9b12.pdf ER -