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Most recent changepoint detection in panel data

机译：Most recent changepoint detection in panel data

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摘要

High dimensional data collection and storage are very common. They are basically large collections of time-series, usually referred to as panel data. This study is interested in structural changes called change-point detection. Similar studies have been conducted by several authors, including Ma and Yau (Ref. 1). Applications for detection of change point occurs in finance, bioinformatics and signal processing. The problem studied pertains to a telecommunications network. Weekly data is kept of the number of events in the network, with the number recorded for each type and for each set of geographical regions. The detection of change point will be useful in planning future events. These event counts vary over time so modeling using change-point is justified. This involves the challenging task of analyzing a large number of time series, one for each event type and region pair. The factors that affect one time series may also affect others. However, there may not be coincidences in time for changes in various events. The study advises the use of a change point method to identify changes in different events. The interest in this study is the most recent change point so that this information can be used in ten predictions of change-points in the future.

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