英文标题：
《Bitcoin Price Prediction: An ARIMA Approach》
---
作者：
Amin Azari
---
最新提交年份：
2019
---
英文摘要：
  Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$.
---
中文摘要：
比特币被认为是世界上最有价值的货币。除了价值极高之外，其价值也经历了急剧增长，从2010年的1美元左右增长到2017年的18000美元左右。近年来，它在包括经济学和计算机科学在内的多个领域引起了相当大的关注。前者主要研究其对市场的影响，确定其价格波动背后的原因，并预测其未来价格。后者主要关注其脆弱性、可扩展性和其他技术加密经济问题。在此，我们旨在通过分析3年期的价格时间序列，揭示传统自回归综合移动平均（ARIMA）模型在预测比特币未来价值方面的有用性。一方面，我们的实证研究表明，这种简单的方案在时间序列行为几乎不变的子时段是有效的，尤其是当它用于短期预测时，例如1天。另一方面，当我们尝试将ARIMA模型训练到一个3年的周期，在此期间比特币价格经历了不同的行为，或者当我们尝试将其用于长期预测时，我们观察到它引入了很大的预测误差。特别是，ARIMA模型无法捕捉价格的剧烈波动，例如2017年底的波动。然后，它要求提取更多的特征，并与价格一起使用，以便更准确地预测价格。我们使用ARIMA模型进一步研究了比特币价格预测，该模型在一个大型数据集上进行训练，并以长度为$w$的比特币价格有限测试窗口作为输入。我们的研究揭示了预测精度、选择（p、q、d$）和窗口大小（w$）之间的相互作用。
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Social and Information Networks        社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
--
一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--

---
PDF下载：
-->