Open AccessA comparative study for Bitcoin cryptocurrency forecasting in period 2017-2019
Author(s) -
Tri Wijayanti Septiarini,
Muhammad Rifki Taufik,
Mufti Afif,
Atika Rukminastiti Masyrifah
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1511/1/012056
Subject(s) - mean squared error , exponential smoothing , autoregressive integrated moving average , adaptive neuro fuzzy inference system , artificial neural network , computer science , cryptocurrency , artificial intelligence , data mining , box–jenkins , moving average , machine learning , statistics , time series , mathematics , fuzzy logic , fuzzy control system , computer security
The objective of this study were (i) to construct the classical statistic and artificial intelligent model for predicting bitcoin cryptocurrency, and (ii) to compare the predicting performance by using root mean square error (RMSE) and mean square error (MSE) as forecasting evaluation tool. The observation data used in this study were collected during January, 5 2017 to October, 1 2019 (in total 1,000 daily observation data). The statistical method used in this study were ARIMA (Autoregressive Moving Average) and Exponential Smoothing. The artificial intelligent model were used in this study were fuzzy time series and ANFIS (Adaptive Neuro Fuzzy Inference System). The partitions data set were of 75%-25% of training and testing, respectively. The cryptocurrency investigated was bitcoin (BTC) which is the top three of most widely traded cryptocurrency. The forecasting results show that the classical method has the smallest value of RMSE and MSE which is exponential smoothing with 9749.81 for MSE and 98.74 for RMSE. However, the performance of forecasting method cannot be guaranteed from either classical or modern forecasting method. Analyzing with different method can be considered for future study, for example machine learning, neural network, modified fuzzy time series, etc.