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2021 | OriginalPaper | Chapter

LSTM Processing of Experimental Time Series with Varied Quality

Authors : Krzysztof Podlaski, Michał Durka, Tomasz Gwizdałła, Alicja Miniak-Górecka, Krzysztof Fortuniak, Włodzimierz Pawlak

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Automatic processing and verification of data obtained in experiments have an essential role in modern science. In the paper, we discuss the assessment of data obtained in meteorological measurements conducted in Biebrza National Park in Poland. The data is essential for understanding the complex environmental processes, such as global warming. The measurements of CO2 flux brings a vast amount of data but suffer from drawbacks like high uncertainty. Part of the data has a high-level of credibility while, others are not reliable. The method of automatic evaluation of data with varied quality is proposed. We use LSTM networks with a weighted square mean error loss function. This approach allows incorporating the information on data reliability in the training process.

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Metadata
Title
LSTM Processing of Experimental Time Series with Varied Quality
Authors
Krzysztof Podlaski
Michał Durka
Tomasz Gwizdałła
Alicja Miniak-Górecka
Krzysztof Fortuniak
Włodzimierz Pawlak
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-77980-1_44

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