Abstract
Missing data frequently happen in environmental research, usually due to faults in data acquisition, inadequate sampling or measurement error. They make difficult to determine whether the limits set by the European Community on certain indicators of air quality are fulfilled or not. Indeed, due to missing values, the number of exceedances per year of \(PM_{10}\), that is particulate matter 10 \(\upmu \)m or less in diameter, and other air quality indicators are often heavily underestimated, and no environmental policy is applied to protect citizen health.
In this paper, we propose a non-parametric method to impute missing values in \(PM_{10}\) time series. It is primarily based on a local polynomial estimator of the trend-cycle in time series. We also compare the proposed method with other methods usually used in literature for the imputation of missing values in univariate time series and implemented in the R package imputeTS.
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Albano, G., La Rocca, M., Perna, C. (2018). On the Imputation of Missing Values in Univariate \(PM_{10}\) Time Series. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_2
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DOI: https://doi.org/10.1007/978-3-319-74727-9_2
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