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A Novel LSTM for Multivariate Time Series with Massive Missingness
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-9009-0999
Umeå University, Faculty of Science and Technology, Department of Computing Science. School of Science and Technology, Aalto University, Aalto, Finland.ORCID iD: 0000-0002-8078-5172
2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 10, article id 2832Article in journal (Refereed) Published
Abstract [en]

Multivariate time series with missing data is ubiquitous when the streaming data is collected by sensors or any other recording instruments. For instance, the outdoor sensors gathering different meteorological variables may encounter low material sensitivity to specific situations, leading to incomplete information gathering. This is problematic in time series prediction with massive missingness and different missing rate of variables. Contribution addressing this problem on the regression task of meteorological datasets by employing Long Short-Term Memory (LSTM), capable of controlling the information flow with its memory unit, is still missing. In this paper, we propose a novel model called forward and backward variable-sensitive LSTM (FBVS-LSTM) consisting of two decay mechanisms and some informative data. The model inputs are mainly the missing indicator, time intervals of missingness in both forward and backward direction and missing rate of each variable. We employ this information to address the so-called missing not at random (MNAR) mechanism. Separately learning the features of each parameter, the model becomes adapted to deal with massive missingness. We conduct our experiment on three real-world datasets for the air pollution forecasting. The results demonstrate that our model performed well along with other LSTM-derivation models in terms of prediction accuracy

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 10, article id 2832
Keywords [en]
multivariate time series, regression, massive missingness, LSTM
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:umu:diva-170904DOI: 10.3390/s20102832OAI: oai:DiVA.org:umu-170904DiVA, id: diva2:1430880
Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2020-05-18Bibliographically approved

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Fouladgar, NazaninFrämling, Kary

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