Poster 128: Identifying Time Series Similarity in Large-Scale Earth System Datasets
TimeThursday, 21 November 20198:30am - 5pm
DescriptionScientific data volumes are growing every day and instrument configurations, quality control and software updates result in changes to the data. This study focuses on developing algorithms that detect changes in time series datasets in the context of the Deduce project. We propose a combination of methods that include dimensionality reduction and clustering to evaluate similarity measuring algorithms. This methodology can be used to discover existing patterns and correlations within a dataset. The current results indicate that the Euclidean Distance metric provides the best results in terms of internal cluster validity measures for multi-variable analyses of large-scale earth system datasets. The poster will include details on our methodology, results, and future work.