séries univariantes de tempo - metodologia de Box & Jenkins

ABSTRACTThe present dissertation is concerned with the method developed byGeorge E. P. Box and Gwilym M. Jenkins.That methodology was developed during the sixties and published by theauthors in 1970 under the title "Time series analysis: forecasting andcontrol".The significance of Box and Jenkins' work is given by the possibilityof constructing linear models of the autoregressive integrated movingaverage type that could describe with precision and parsimony the stochasticprocess that generates a time series, offering therefore accurateforecasts.The strategy in that methodology consists in identifying models ableto describe the data available, to efficiently estimate its parametersand to evaluate the model "vis-S-vis" the phenomenon being invéstigated.If this representation of the real world is adequate, the model can beapplied in time series analysis or, on the contrary, if that representationis not adequate, a new model should be designed and a new roundof tests run.The present dissertation is structured in seven chapters.Chapter I consists of not only the general question being posed andour concern about it justified, but we also develop the fundamentalideas to deal with time series analysis: a short history of the subject,the importance and the statiscal meaning of the technique and the conceptproper of time series.In the chapter II we develop the basic concepts of Stochastic Processthat will be useful for the understanding and working of Box andJenkins' methodology.In chapters III, IV and V we deal in detail with Box and Jenkins 1 timeseries analysis: the models they use, the process of modeling and themethod for forecasting.In chapter VI, we apply the proceding developments to two sets of data,which are significant to the understanding of Rio Grande do Sul economy:the Consumer Price Index (cost of living index) in the city of PortoAlegre and Electric Energy Comsumption in the State. 11 can be observedthat the forecasts obtained through Box and Jenkins' method showvalues very much close to the real data, making it clear about theusefulness of the author's methodology.The last chapter is a summary of the method discussed. We call attentionto its qualities but also to its operational shortcomings and we referto some other methods for time series analysis, trying to locate aspectsfor applying each one of them.