Handling missing data: applications to environmental analysis

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G. Latini, G. Passerini
WIT Press, 2004 - Mathematics - 185 pages
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Designed for use as a practical guide, this volume features a wide range of techniques for analyzing and filling gaps in time series data. It starts with a description of the methods that can be used to replace absent data based on the distribution of the gaps and then classifies the latter according to their length. Various specialized algorithms and techniques are also detailed.Particular attention is paid to "nearest neighbour" techniques that are able to fill gaps of up to eight hours, while Auto Regressive Models and Artificial Neural Networks (ANN) with the ability to fill longer gaps are also explored. ANN's various architectures, features and their application to air quality time series remediation are highlighted.The text also contains an extensive literature review.

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Imputation techniques
Data validation and data gaps in environmental
Statistical modelling of the remediation

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