DaSSWeb – Data Science and Statistics Webinar
Tuesday 1 June, 14:30
Speaker: José G. Dias ISCTE Business School
Title: Model-based clustering of time series data
Zoom link: videoconf-colibri.zoom.us/j/84508964379
Abstract: In the digital age, data streams have been produced at an increasing pace from different sources for instance from biometric devices (sensors) and stock market (high frequency) data to digital platforms (feeds, audio, video). This type of data, measured on one or more variables over time (or sequence), is called time series, panel, or more generally longitudinal data. Time-dependent modeling has been applied in many contexts not only forecasting, but also outlier detection, matching, clustering, indexing, etc. This talk discusses the use of finite mixture models in time series clustering. First, I present the overall finite mixture framework. Then, a second level of analysis is added to model sequences within each observation. An application to COVID time series data illustrates the main concepts. The talk concludes with a brief discussion in the context of cross-sectional, dynamic clustering, and biclusteringwith implications for density estimation, outlier detection, and measurement error modeling.
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