Ethan Schaeffer, Penn State University

Class Year:

2016

Mentor:

Vyacheslav Lyubchich, Ph.D.

Abstract:

Climate data sets available currently, whether these are paleo-reconstructions, long-term weather monitoring records, or remote sensing data, contain a whelm of space-time information that needs to be analyzed under the pressure of computational and data storage requirements. This has led to a spark of interest in dynamic space-time clustering algorithms that are particularly suitable in the analysis of data streams. The trend-based clustering algorithm TRUST allows for space-time clustering in real time. However, this method requires the user to set a number of tuning parameters by hand. Here we propose a data-driven approach to automatically select the tuning parameters based on a penalized loss function. We focus on the two most important parameters of the TRUST algorithm: the current likeness of observations across the slide level and the temporal persistence within an analyzed window. We demonstrate the performance of the enhanced clustering procedure using simulated time series and illustrate its applicability using long-term records of water temperature in the Chesapeake Bay.

Location:

Chesapeake Biological Laboratory