Greedy copula segmentation of multivariate non-stationary time series for climate change adaptation
Abstract
Non-stationary climate data are often encountered in dealing with natural hazards, climate change and disaster reduction. With drought, for instance, it is common to encounter such non-stationary data sets (time series). The objectives of this work are to formulate a rational data-driven approach that can consider non-stationary and time series on multiple random variables that can have generalized underlying probability distributions and dependence structures. The methodology proposed seeks to divide up the data into non-overlapping segments, each of which is treated as stationary with some underlying probability and dependence structure, while the long time series yields multiple such segments that are mutually independent. The Greedy Copula Segmentation (GCS) algorithm developed employs best-fit probability distributions and copula functions after data-driven time series segmentation. Validation of the proposed methodology is demonstrated using a benchmark problem as well as a single-site realistic drought example. The proposed GCS approach has potential use in climate change adaptation (CCA) and disaster risk reduction (DRR) for any climate-related hazards involving non-stationary time series data.
