Long-term missing value imputation for time series data using deep neural networks

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published 106102 1 322329 document 106102 indexcodes.txt text/x-c++ 40effc8ef554ea00380d7c13d83da389 MD5 14566 2024-05-13 09:19:56 https://pure.iiasa.ac.at/id/eprint/19715/2/indexcodes.txt 19715 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/106100 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/106100 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/106100 archive 2 disk0/00/01/97/15 2024-05-13 09:11:50 2024-05-13 09:11:50 2024-05-13 09:11:50 article show Park J. Müller J. Arora B. Faybishenko B. Pastorello G. Varadharajan C. Sahu R. 2309 0000-0003-0681-0509 Agarwal D. Long-term missing value imputation for time series data using deep neural networks pub BNR WAT We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations. 2022-12-23 doi:10.1007/s00521-022-08165-6 3198 public Neural Computing and Applications 35 9071-9091 TRUE 0941-0643 https://github.com/JanghoPark-LBL/Dataset_Missing_Value_Imputation_with_Deep_Neural_Networks data info:eu-repo/semantics/article