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.
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Notes
- 1.
\(\mathcal {I}(u)\) is 1 if the condition u is fulfilled, 0 otherwise.
- 2.
\(|u|_{\epsilon }^2\) is u if \(u \ge \epsilon \), 0 otherwise.
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Acknowledgements
Vincenzo Capone developed part of the work as final dissertation for B. Sc. in Computer Science, under supervision of F. Camastra, at University Parthenope of Naples.
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Camastra, F., Capone, V., Ciaramella, A., Landi, T.C., Riccio, A., Staiano, A. (2021). Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_3
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