Large-scale Retrieval of Bayesian Machine Learning Models for Time Series Data via Gaussian Processes Article Swipe
Fabian Berns
,
Christian Beecks
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5220/0010109700710080
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5220/0010109700710080
Gaussian Process Models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable timeseries interpolation, regression, and classification. These models are frequently instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default instantiations yield acceptable analytical quality in terms of model accuracy, GPM retrieval algorithms automatically search for an application-specific model fitting a particular dataset. State-of-the-art methods for automatic retrieval of GPMs are searching the space of possible models in a rather intricate way and thus result in super-quadratic computation time complexity for model selection and evaluation. Since these properties only enable processing small datasets with low statistical versatility, we propose the Timeseries Automatic GPM Retrieval (TAGR) algorithm for efficient retrieval of large-scale GPMs. The resulting model is composed of i ndependent statistical representations for non-overlapping segments of the given data and reduces computation time by orders of magnitude. Our performance analysis indicates that our proposal is able to outperform state-of-the-art algorithms for automatic GPM retrieval with respect to the qualities of efficiency, scalability, and accuracy.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0010109700710080
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4254351220
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https://openalex.org/W4254351220Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5220/0010109700710080Digital Object Identifier
- Title
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Large-scale Retrieval of Bayesian Machine Learning Models for Time Series Data via Gaussian ProcessesWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2020Year of publication
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2020-01-01Full publication date if available
- Authors
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Fabian Berns, Christian BeecksList of authors in order
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https://doi.org/10.5220/0010109700710080Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5220/0010109700710080Direct OA link when available
- Concepts
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Computer science, Gaussian process, Series (stratigraphy), Time series, Bayesian probability, Scale (ratio), Artificial intelligence, Machine learning, Data mining, Gaussian, Pattern recognition (psychology), Biology, Quantum mechanics, Physics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.instantiations | 45 |
| abstract_inverted_index.interpolation, | 19 |
| abstract_inverted_index.classification. | 22 |
| abstract_inverted_index.non-overlapping | 138 |
| abstract_inverted_index.representations | 136 |
| abstract_inverted_index.super-quadratic | 91 |
| abstract_inverted_index.State-of-the-art | 68 |
| abstract_inverted_index.state-of-the-art | 163 |
| abstract_inverted_index.application-specific | 62 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.50801043 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |