Descripción
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We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions. | |
Internacional
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Si |
Nombre congreso
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26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Tipo de participación
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960 |
Lugar del congreso
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Salerno (Italy) |
Revisores
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Si |
ISBN o ISSN
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978-1-5090-0746-2 |
DOI
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10.1109/MLSP.2016.7738844 |
Fecha inicio congreso
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13/09/2016 |
Fecha fin congreso
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16/09/2016 |
Desde la página
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1 |
Hasta la página
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6 |
Título de las actas
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Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |