Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling Article Swipe

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Jan David Hüwel , Florian Haselbeck , Dominik G. Grimm , Christian Beecks ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1007/978-3-031-15791-2_10

One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing E vent- T riggered K ernel A djustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA’s ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.

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Computer science Gaussian process Data stream mining Data stream Kernel (algebra) Gaussian Data mining Process (computing) Point (geometry) Gaussian function Algorithm Mathematics Physics Quantum mechanics Combinatorics Telecommunications Operating system Geometry

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Type: book-chapter
Language: en
Landing Page: https://doi.org/10.1007/978-3-031-15791-2_10
PDF: https://link.springer.com/content/pdf/10.1007/978-3-031-15791-2_10.pdf
OA Status: hybrid
Cited By: 4
References: 15
Related Works: 10
OpenAlex ID: https://openalex.org/W4296195565

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DOI: https://doi.org/10.1007/978-3-031-15791-2_10

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Title: Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling

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Type: book-chapter

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Language: en

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Publication year: 2022

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Publication date: 2022-01-01

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Authors: Jan David Hüwel, Florian Haselbeck, Dominik G. Grimm, Christian Beecks

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Landing page: https://doi.org/10.1007/978-3-031-15791-2_10

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Concepts: Computer science, Gaussian process, Data stream mining, Data stream, Kernel (algebra), Gaussian, Data mining, Process (computing), Point (geometry), Gaussian function, Algorithm, Mathematics, Physics, Quantum mechanics, Combinatorics, Telecommunications, Operating system, Geometry

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Cited by: 4

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