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Spectral decomposition method has been used in obtaining statistical control of time series data analyzed providing quick response in detecting out of control state of the process compared to that of Shewhart control charts.

In this paper, is analised the behaviour of the distribution of squared-residual autocorrelations functions residuals used for diagnostic checking of AR models representative of time series.

Principal aspects of sampling theory Nand calculating processes used in determining the size of a sample, categorized and group with proportional and optimum distribution in case of a random sample, are presented in this paper.

In this work, will be presented the basic concept of Bayesian methodology, as well as the use of a simulated serie to check the sensibility of SBPC program to identify changes of state.

Reliability is defined as error of measurement. Reliability theory is described and a model is proposed that divides the total variance in two parts: the variance due to error and the real variance.The method of Split-half is described and to determine the reliability of the instrument “Deslizador Manual” used for time control in the area of Physical Education.

In this paper, the methodology of Box-Jenkins is presented and an intervention model is developed to analyse the behavior of economic growth and the inflationary process during the period of January. 1965 to December 1982, utilizing the growth rates of Gross Internal Product and the monthly percentáge increase of price levels. The analysis shows the periods of acceleration and deceleration of economic growth and of inflation.

ln this paper, the factorial analysis was applied to verify the behaviour of the Cost of Living lndex of Brazil, from january 1980 to December 1984.

Na presente dissertação, além de se expor a fundamentação teórica das Metodologias Estruturais clássica e bayesiana para previsão de séries temporais, analisou-se o comportamento de séries temporais, analizou-se o comportamento da série produção de sacos de polipropileno produzidos pela Indústria Têxtil Oeste Ltda. com observações cobrindo o período de janeiro de 1987 a junho de 1992. Na análise, através dos pacotes computacionais correspondentes: STAMP (clássico) e BATS (bayesiano), utilizou-se variáveis de intervenção e a variável exógena correspondente à produção de metros quadrados de polipropileno, cobrindo período idêntico, ou seja janeiro de 1987 a junho de 1992. Adotando como critério de decisão o erro médio quadráticas previsões no período de ajuste e da análise ex-ante feita com as seis últimas observações (janeiro a junho de 1992), para testar a capacidade extrapolativa dos modelos, escolheu-se um modelo representativo dentro de cada abordagem e em seguida foi feito um estudo comparativo de ambas. In the present dissertation, besides exposing the theoretical foundations of Structural Models (Classic and Bayesian approaches); we also analysed the series of production of propileno´s bag produced by Indústria Têxtil Oeste Ltda. with observation covering the period from January 1987 to June 1992. We used in the analysis the packages: STAMP (classical) and BATS (Bayesian), with intervention variables and the series of production of square meters of propileno as explanatory variable. As decision criterion we used the mean square error during the period of adjustment and the ex-ante analysis with the last six observation (January up to June of 1992), to test the predictive ability of the models.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR Este trabalho associa previsão de Séries Temporais a uma nova metodologia de processamento de informação: REDE NEURAL. Usaremos o modelo de Retropropagação, que consiste em uma Rede Neural multicamada com as unidades conectadas apenas com a unidades conectadas apenas com as unidades da camada subseqüente e com a informação passando em uma única direção. Aplicaremos o modelo de retropropagação na análise de quatro séries temporais: uma série ruidosa. Uma série com tendência, uma série sazonal e uma série de Consumo de Energia Elétrica da cidade de Uruguaiana, RS. Os resultados obtidos serão comparados com os modelos ARIMA de Box e Jenkins e um modelo com intervenção This work join the Times-Séries Forecasting to a new information processing metodoligy: NEURAL NETWORK. We will use the Back-Propagation model, that consist in an arquitecture of a feed-forward network with hidden layers. We will apply the Back-Propagation model in an analysis to four times series: a noisy series, a series with trend, a seasonal series and an electrical energy consuption series of Uruguaiana, RS. The results will be compare with the Box and jenkins´ ARIMA models and a model with intervention.