Nonstationary Time Series Models on Cryptocurrencies

Abstract

Cryptocurrencies are known as unpredictable due to their highly volatility. In time series, the forecasting accuracy is strongly affected by the methodologies that are used in identifying the pattern of a nonstationary stochastic realization. The purpose of the present study is to develop an algorithm that is capable of efficiently identifying the pattern of cryptocurrencies. A brief summary of the algorithm is given. To illustrate the quality of our proposed algorithm, we study the pattern of ten different reputable cryptocurrencies and use their daily closing prices to constitute a time series. The comparison between our proposed forecasting algorithm versus the autoregressive integrated moving average (ARIMA) process will be demonstrated.