Prediction of environmental missing data time series by Support Vector Machine Regression and Correlation Dimension estimation. (April 2022)

• Record Type: Journal Article
• Authors: Camastra, Francesco; Capone, Vincenzo; Ciaramella, Angelo; Riccio, Angelo; Staiano, Antonino
• Is Part Of: Environmental modelling & software. Volume 150(2022)
• Publication Date: 2022-04
• Subjects: Missing data -- Model order estimation -- Grassberger-procaccia algorithm -- Correlation dimension estimation -- Hough transform -- Support vector machine regression; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105343
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental time series are often affected by missing data, namely data unavailability at certain time points. This paper presents the Iterated Imputation and Prediction algorithm, that allows the prediction of time series with missing data. The algorithm uses iteratively the Correlation Dimension Estimation of the underlying dynamic system generating the time series to fix the model order (i.e., how many past samples are required to model the time series accurately), and the Support Vector Machine Regression to estimate the skeleton of time series. Experimental validation of the algorithm on three environmental time series with missing data, expressing the concentration of Ozone in three European sites, shows a small average percentage prediction error for all time series on the test set. Highlights: The paper presents Iterated Imputation and Prediction (IIP) algorithm for the missing data time series prediction . IIP uses Correlation Dimension and Support Vector Machine Regression to estimate the model order and the skeleton of time series. Correlation Dimension is estimated with the proposed Grassberger-Procaccia-Hough algorithm.