A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:15:25Z
dc.date.available	2014-03-01T01:15:25Z
dc.date.issued	2000	en
dc.identifier.issn	0043-1397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13485
dc.subject	Stochastic Simulation	en
dc.subject	Time Series	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Limnology	en
dc.subject.classification	Water Resources	en
dc.subject.other	DISAGGREGATION	en
dc.title	A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/2000WR900044	en
heal.identifier.secondary	http://dx.doi.org/10.1029/2000WR900044	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	A generalized framework for single-variate and multivariate simulation and forecasting problems in stochastic hydrology is proposed. It is appropriate for short-term or long-term memory processes and preserves the Hurst coefficient even in multivariate processes with a different Hurst coefficient in each location. Simultaneously, it explicitly preserves the coefficients of skewness of the processes. The proposed framework incorporates short-memory (autoregressive moving average) and long-memory (fractional Gaussian noise) models, considering them as special instances of a parametrically defined generalized autocovariance function, more comprehensive than those used in these classes of models. The generalized autocovariance function is then implemented in a generalized moving average generating scheme that yields a new time-symmetric (backward-forward) representation, whose advantages are studied. Fast algorithms for computation of internal parameters of the generating scheme are developed, appropriate for problems including even thousands of such parameters. The proposed generating scheme is also adapted through a generalized methodology to perform in forecast mode, in addition to simulation mode. Finally, a specific form of the model fur problems where the autocorrelation function can be defined only for a certain finite number of lags is also studied. Several illustrations are included to clarify the features and the performance of the components of the proposed framework.	en
heal.publisher	AMER GEOPHYSICAL UNION	en
heal.journalName	WATER RESOURCES RESEARCH	en
dc.identifier.doi	10.1029/2000WR900044	en
dc.identifier.isi	ISI:000087226800014	en
dc.identifier.volume	36	en
dc.identifier.issue	6	en
dc.identifier.spage	1519	en
dc.identifier.epage	1533	en