Online detection of continuous changes in stochastic processes
Online detection of continuous changes in stochastic processes
复制标题
在线检测随机过程的连续变化
DOI：
10.1007/s41060-017-0045-2
复制
发表时间：
2017
影响因子：
2.4
通讯作者：
Yamanishi Kenji
中科院分区：
文献类型：
--
作者：
Miyaguchi Kohei;Yamanishi Kenji
我们关心的是检测随机过程中的连续变化。在非平稳随机过程的传统研究中，经常假设变化是突然发生的。相反，我们假设它们连续发生。该方案包括一个有效的算法和严格的理论分析下的连续性假设。本文的贡献如下：我们首先提出了一个新的连续变化过程的表征。我们还提出了一个时间和空间有效的在线估计的特点。然后，采用所提出的估计，我们提出了一种方法来检测变化，以及调整其超参数的标准。最后，所提出的方法被证明是有效的，通过实验，涉及现实生活中的数据，从市场，服务器和工业机器。
We are concerned with detecting continuous changes in stochastic processes. In conventional studies on non-stationary stochastic processes, it is often assumed that changes occur abruptly. By contrast, we assume that they take place continuously. The proposed scheme consists of an efficient algorithm and rigorous theoretical analysis under the assumption of continuity. The contribution of this paper is as follows: We first propose a novel characterization of processes for continuous changes. We also present a time- and space-efficient online estimator of the characteristics. Then, employing the proposed estimate, we propose a method for detecting changes together with a criterion for tuning its hyper-parameter. Finally, the proposed methods are shown to be effective through experimentation involving real-life data from markets, servers, and industrial machines.