Handling missing data : applications to environmental analysis / editors, G. Latini & G. Passerini.

CONTENT


Chapter 1 An introduction to the statistical filling of environmental data times series
1.1 Classification of missing data
1.2 Linear interpolation
1.3 Imputation

Chapter 2 Data validation and data gaps in environmental time series
2.1 Monitoring stations
2.2 Analysis and validation of time series

Chapter 3 Statistical modelling of the remediation of environmental data time series
3.1 Statistical modelling applicable to time series
3.2 Autoregressive models and time series
3.3 Development of models for forecast and remediation

Chapter 4 Imputation techniques for meteorological and air quality data filling
4.1 Imputation techniques and missing data
4.2 Nearest neighbour techniques
4.3 Spatial interpolation
4.4 The voronoi diagram
4.5 Statistic filling of sparse time series

Chapter 5 Neural networks and their applications to meteorological and air quality data filling
5.1 Introduction to Neural networks
5.2 Neural networks in data remediation
5.3 A survey on Neural network applications in meteorological and air quality fields
5.4 Building up networks for remediation of time series
5.5 Are the ANN applicable to the remediation of time series?

References : p. 181 - 185