论文标题
马尔可夫链的线性定律，并应用比特币价格的异常检测
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices
论文作者
论文摘要
本文的目标是双重的：（1）提出一种能够找到马尔可夫链时间演变的线性法律的新方法，以及（2）将这种方法应用于比特币价格的异常检测。为了实现这些目标，首先，通过使用（分类）自相关功能的时间嵌入马尔可夫链的线性定律。然后，由比特币汇率的第一个差异（违反美国美元）产生了二进制系列。最后，描述该系列线性定律的最小参数数量是通过阶梯时间窗口确定的。根据结果，线性定律通常在两个时期内变得更加复杂（包含一个额外的第三个参数，表明隐藏的马尔可夫属性）：在Covid-19偶然的加密货币市场崩溃之前（2020年3月12日），以及比特币价格破纪录的激增之前（Q4 2020-22020-Q1 20211）。此外，该第三个参数的局部值通常与短期价格峰有关，这表明价格操纵。
The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.
