NONSTATIONARY TIME SERIES MODELS ON CRYPTOCURRENCIES

被引：4

作者：

Shih, Shou Hsing ^{[1
]}

机构：

[1] Amer Univ Sharjah, Dept Math & Stat Sharjah, POB 26666, Sharjah, U Arab Emirates

来源：

ECONOMICS, FINANCE AND STATISTICS, VOL 2, ISSUE 1 | 2018年

关键词：

Time Series Forecasting; Cryptocurrencies; Polynomial Trend Analysis; ARIMA;

D O I：

10.26480/icefs.01.2018.01.05

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：1 / 5

页数：5

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