Fractal-based Analysis to Identify Trend Changes in Multiple Climate Time Series

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Ana Maria Heuminski de Avila , Luciana Alvim Santos Romani

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2011   anomalies data streams clustering

In the last few decades, huge amounts of climate data have been gathered and stored by several institutions. The analysis of these data has become an important task due to worldwide climate changes and the consequent social and economic effects. In this work, we propose an approach to analyzing multiple climate time series in order to identify intrinsic temporal patterns and trend changes. By dealing with multiple time series as multidimensional data streams and combining fractal-based analysis with clustering, we can integrate different climate variables and discover general behavior changes over time.