Studies on Time Series Applications in Environmental Sciences by Alina Bărbulescu

Author:Alina Bărbulescu
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

(2)Generation of the data at the upper level using a model specified by the user and the computation of ;

(3)Choosing the closest k neighbours (k-NN) by the estimation of the distances between and

(4)Building the vector Z, whose first d − 1 elements are those from the step (1) and the last one is ;

(5)Rotate Z in the original space;

(6)Repeat the steps (2)–(5) until the generation of all data.

Lee et al. [22] proposed a disaggregation procedure that uses parts of the two algorithms presented. KNNR is employed for finding the values from the lower level whose sum is close to the values previously generated at the upper level. Then, the adjusting procedure is applied, to fulfill the additivity condition.

The original idea of this approach consists in the inclusion of the value from the last season of the previous year in the choice of the sequence at the lower level and the use of a genetic algorithm for detecting the variable at that level.

A competitive monthly multisite model must be capable of reproducing the relevant properties of the observed precipitation, as the spatial and temporal dependence, the marginal distribution functions (at each station) at monthly and annual scale. It also must preserve the properties of the extreme events (intensity, severity, duration, the distance between the events), etc. [29].

Generation of monthly precipitation at many sites can be done by the disaggregation of the annual precipitation generated using the fragments methods [37], the method of synthetic fragments [26] or the modified method of synthetic fragments [23], by minimizing the sum:

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