2024
DOI: 10.1109/tvcg.2024.3364388
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Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Tim Krake,
Daniel Klötzl,
David Hägele
et al.

Abstract: Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertaintyaware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling base… Show more

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Cited by 3 publications
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