NAB (Numenta Anomaly Benchmark)

第一个旨在评估实时异常检测器基准的时间基准 物联网的发展创造了大量的流数据。在这些数据中发现异常可以为机会或失败提供有价值的见解。然而，由于需要实时处理数据、不断学习和做出预测，这很难实现。我们如何评估和比较各种实时异常检测技术？ Numenta Anomaly Benchmark (NAB) 提供了一个标准的开源框架，用于评估流数据上的实时异常检测算法。通过受控、可重复的开源工具环境，NAB 奖励能够尽快发现异常、不触发误报并自动适应任何变化的统计数据的检测器。 NAB 包含两个主要组件：为流数据设计的评分系统和带有标记的真实时间序列数据的数据集。

As the first benchmark designed to evaluate real-time anomaly detectors, the proliferation of the Internet of Things (IoT) has generated vast amounts of streaming data. Detecting anomalies within these data can provide valuable insights into potential opportunities or unexpected failures. However, this task is challenging to accomplish due to the requirements of real-time data processing, continuous learning, and real-time prediction making. How can we evaluate and compare different real-time anomaly detection technologies? The Numenta Anomaly Benchmark (NAB) offers a standardized open-source framework for assessing real-time anomaly detection algorithms on streaming data. Through a controlled and reproducible open-source tooling environment, NAB incentivizes detectors that can identify anomalies as early as possible, avoid false alarms, and automatically adapt to any shifting statistical distributions. NAB comprises two primary components: a scoring system tailored for streaming data, and a dataset of labeled real-world time-series data.

搜集汇总

背景与挑战

背景概述

NAB (Numenta Anomaly Benchmark) 是一个开源框架，旨在为流数据提供实时异常检测算法的评估标准。它包含一个评分系统和带标签的真实时间序列数据集，用于在可重复环境中比较检测器的性能，并发布于2015年。该数据集专注于时间序列数据分析，适用于异常检测任务。