Change Detection in Climate Time Series Based on Bounded-Variation Clustering
Climate time series are generally nonstationary which means that their statistical properties change with time. Analysis of nonstationary time series requires detecting of change points between a set of clusters, where model of time series in each cluster has different statistical parameters. Common change detection methods are based on assumptions that may not be valid generally. Bounded-variation clustering can solve the change detection problem with minimum restrictive assumptions. In this paper, this method is employed to detect the pattern of changes in the Pacific Decadal Oscillation and the piecewise linear trend of US temperature. An optimal number of the change points are found with the Bayesian information criterion.
KeywordsTime series Non-stationary Change detection Abrupt climate change Autocorrelation
This work is supported by the Expeditions in Computing by the National Science Foundation under Award Number: CCF-1029731: Expedition in Computing: Understanding Climate Change.
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