We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features (RFs) to efficiently use the Maximum Mean Discrepancy as a distance between distributions, furthermore exploiting recent optical hardware to compute high-dimensional RFs in near constant time. We show that our method is significantly faster than usual non-parametric methods for a given accuracy.

中文翻译：

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NEWMA：一种可扩展的无模型在线变化点检测新方法

我们考虑在计算能力和内存有限的情况下检测多维时间序列分布的突然变化的问题。在本文中，我们提出了一种新的、简单的无模型在线变化点检测方法，该方法仅依赖于快速和轻量级的递归统计，受到经典指数加权移动平均算法 (EWMA) 的启发。提出的想法是计算具有不同遗忘因子的数据流的两个 EWMA 统计数据，并比较它们。通过这样做，我们表明我们隐式地将最近的样本与较旧的样本进行比较，而无需显式存储它们。此外，我们利用随机特征 (RF) 有效地使用最大平均差异作为分布之间的距离，此外，还利用最近的光学硬件在近乎恒定的时间内计算高维 RF。我们表明，对于给定的精度，我们的方法比通常的非参数方法要快得多。