Title: Micro-macro changepoint inference for periodic data sequences
Authors: Rebecca Killick - Lancaster University (United Kingdom) [presenting]
Abstract: Existing changepoint approaches consider changepoints to occur linearly in time; one changepoint happens after another, and they are not linked. However, data processes may have regularly occurring changepoints, e.g. a yearly increase in ice-cream sales on the first hot weekend. Using linear changepoint approaches here will miss more global features such as a decrease in ice-cream sales in favour of sorbet. Being able to tease these global changepoint features from the more local(periodic) ones is beneficial for inference. We propose a periodic changepoint model to model this behaviour using a mixture of a periodic and linear time perspective. Built around a Reversible Jump Markov Chain Monte Carlo sampler, the Bayesian framework is used to study the local (periodic) changepoint behaviour. We integrate the local changepoint model into the pruned exact linear time (PELT) search algorithm to identify the optimal global changepoint positions. We demonstrate that the method detects both local and global changepoints with high accuracy on simulated and motivating environmental \& economic applications that share periodic behaviour.