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Detecting recent changepoints in time-series can be important for short-termprediction, as we can then base predictions just on the data since thechangepoint. In many applications we have panel data, consisting of manyrelated univariate time-series. We present a novel approach to detect sets ofmost recent changepoints in such panel data which aims to pool informationacross time-series, so that we preferentially infer a most recent change at thesame time-point in multiple series. Our approach is computationally efficientas it involves analysing each time-series independently to obtain a profilelikelihood like quantity that summarises the evidence for the series havingeither no change or a specific value for its most recent changepoint. We thenpost-process this output from each time-series to obtain a potentially smallset of times for the most recent changepoints, and, for each time, the set ofseries which has their most recent changepoint at that time. We demonstrate theusefulness of this method on two data sets: forecasting events in atelecommunications network and inference about changes in the net asset ratiofor a panel of US firms.

Most recent changepoint detection in Panel data