Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as mobile phones or industrial sensors. In these scenarios it may be beneficial to trade the cost of collecting an environmental measurement against the quality or "fidelity" of this measurement and how the measurement affects changepoint estimation. For instance, one might decide between inertial measurements or GPS to determine changepoints for motion. A Bayesian approach to changepoint detection is particularly appealing because we can represent our posterior uncertainty about changepoints and make active, cost-sensitive decisions about data fidelity to reduce this posterior uncertainty. Moreover, the total cost could be dramatically lowered through active fidelity switching, while remaining robust to changes in data distribution. We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints. We evaluate this framework on synthetic, video, and audio data and show that this information-based approach results in accurate predictions while reducing total cost.

翻译：用于检测变化点或时间序列行为突然变化的在线算法,往往以有限的资源部署,例如,用于边缘计算环境,如移动电话或工业传感器。在这些假设中,它可能有利于将收集环境测量的成本与这一测量的质量或“不忠”和测量如何影响变化点估计进行交易。例如,在惯性测量或全球定位系统之间作出决定,以确定运动变化点。贝叶西亚对变化点探测的方法特别具有吸引力,因为我们可以代表我们的后端变点的不确定性,并且对数据忠诚性作出积极、成本敏感的决定,以减少这种后端不确定性。此外,通过积极忠诚性转换,总成本可以大幅降低,同时保持对数据分配变化的强劲。我们提议一种多忠诚性方法,根据改变点的最大信息收益,就哪些数据忠诚性进行成本敏感的决定。我们评估关于合成、视频和音频数据的这一框架,并表明这种基于信息的方法在准确预测中产生结果,同时降低总成本。