In river flow analysis and forecasting there are some key elements to consider in order to obtain reliable results. For example, seasonality is often accounted for in statistical models because climatic oscillations occurring every year have an obvious impact on river flow. Further sources of alteration could be caused by changes in reservoir management, instrumentation or even unexpected shifts in climatic conditions. When these changes are ignored the statistical results can be strongly misleading. This paper develops an automatic procedure to estimate number and locations of changepoints in Periodic AutoRegressive models. These latter have been extensively used for modelling seasonality in hydrology, climatology, economics and electrical engineering, but there are very few papers devoted also to changepoints detection, moreover being limited to changes in mean or variance. In our proposal we allow
The GARCH models have been found difficult to build by classical\ud
methods, and several other approaches have been proposed in literature,\ud
including metaheuristic and evolutionary ones. In the present paper we employ\ud
Genetic Algorithms to estimate the parameters of GARCH(1,1) models,\ud
assuming a fixed computational time (measured in number of fitness function\ud
evaluations) that is variously allocated in number of generations, number of\ud
algorithm restarts and number of chromosomes in the population, in order to\ud
gain some indications about the impact of each of these factors on the estimates.\ud
Results from this simulation study show that if the main purpose is to\ud
reach a high quality solution with no time restrictions the algorithm should\ud
not be restarted and an average population size is recommended, while if the\ud
interest is focused on driving rapidly to a satisfactory solution then for moderate\ud
population sizes it is convenient to restart the algorithm, even if this\ud
means to have a small number of generations
The detection of outliers in a time series is an important issue because their presence may have serious negative effects on the analysis in many different ways. Moreover the presence of a complex seasonal pattern in the series could affect the properties of the usual outlier detection procedures. Therefore modelling the appropriate form of seasonality is a very important step when outliers are present in a seasonal time series. In this paper we present some procedures for detection and estimation of additive outliers when parametric seasonal models, in particular Periodic AutoRegressive, are specified to fit the data. A simulation study is presented to evaluate the benefits and the drawbacks of the proposed procedure on a selection of seasonal time series. An application to three real time series is also examined.
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