Environmental time series are often affected by missing data, namely data unavailability at certain time points. In this paper, it is presented an Iterated Prediction and Imputation algorithm, that makes possible time series prediction in presence of missing data. The algorithm uses Dynamics Reconstruction and Machine Learning methods for estimating the model order and the skeleton of time series, respectively. Experimental validation of the algorithm on an environmental time series with missing data, expressing the concentration of Ozone in a European site, shows an average percentage prediction error of 0.45 % on the test set.

Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction

Camastra F.
;
Ciaramella A.;Riccio A.
Membro del Collaboration Group
;
Staiano A.
2021

Abstract

Environmental time series are often affected by missing data, namely data unavailability at certain time points. In this paper, it is presented an Iterated Prediction and Imputation algorithm, that makes possible time series prediction in presence of missing data. The algorithm uses Dynamics Reconstruction and Machine Learning methods for estimating the model order and the skeleton of time series, respectively. Experimental validation of the algorithm on an environmental time series with missing data, expressing the concentration of Ozone in a European site, shows an average percentage prediction error of 0.45 % on the test set.
978-3-030-68779-3
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/96870
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