  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.
