Analysis and Forecasting of Blockchain-based Cryptocurrencies and Performance Evaluation of TBATS, NNAR and ARIMA

The rapid growth of cryptocurrencies has gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. Therefore the intelligent management and under-standing the characteristics of cryptocurrencies are becoming more interesting. The price of cryptocurrencies are characterized by linear and nonlinear trend, seasonality and high volatility, which increases the risk factors for investors. This study ex-periments with three different time series forecasting methods, specifically considered for Cryptocurrencies price such as Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR) and Cardano (XRP), and devises a procedure to evaluate their performance. Time series data are collected and examined using descriptive statistics. In next step, the White Neural Network is used for Non-Linearity and Dickey-Fuller for nonstationary and correlation among different settings of datasets. Based on these analyses, we evaluate efficient financial forecasting models such as Autoregressive Integrated Moving Average (ARIMA), Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) and Neural Network Autoregressive (NNAR) with reference to different parameters configuration of these models. The performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criterion and models are ranked by statistical mean and standard deviation of MAPE values. The NNAR model gives minimum MAPE of 2.823 while the minimum convergence time of 4.9835s is observed with TBATS and hence, these are ranked at top amongst other models respectively. These results underpin that neural network-based models perform equally well on both types of nonlinear and linear financial data and, thus, have the potential to improve the impact of financial transaction and cryptocurrencies price bringing more innovation in the decision making process.