Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques

Climate change can be detected in several scientific domains including hydrology, meteorology, and oceanography. In this paper we describe our on-going work for detecting change in multivariate time series data from these domains. For the detection, we extract climate patterns from the data, represented by clusters of time series, and trace the clusters over time. A climate pattern is categorized as a changing pattern if it shows a similar tendency over a significant amount of time, e.g. several years. Since existing clustering and cluster tracing approaches are not suitable for time series data, we are working on novel clustering and tracing approaches specifically for this purpose.