Data remediation of long gaps in time series requires a precise knowledge and a subsequent modelling of the statistical structure of time series themselves. The techniques for data remediation of long gaps and the related results are somewhat similar to those of prediction models and techniques. The main effort of environmental scientists, researchers and analysts has always been predicting the future behaviour of the analysed variables.Plenty of statiscal methods have been developed for the issue or applied in the field. On such basis, in this chapter , the authors provide a review of techniques for examining and modelling the statistical structure of observation series collected at equally spaced time intervals.
Statistical Modelling for remediation of environmental-data time
COCCI GRIFONI, ROBERTA;
2004-01-01
Abstract
Data remediation of long gaps in time series requires a precise knowledge and a subsequent modelling of the statistical structure of time series themselves. The techniques for data remediation of long gaps and the related results are somewhat similar to those of prediction models and techniques. The main effort of environmental scientists, researchers and analysts has always been predicting the future behaviour of the analysed variables.Plenty of statiscal methods have been developed for the issue or applied in the field. On such basis, in this chapter , the authors provide a review of techniques for examining and modelling the statistical structure of observation series collected at equally spaced time intervals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
