Os processos espaço-temporais vêm ganhando destaque nos últimos anos, em razão do aumento de estudos compreendendo variáveis que apresentam interação entre as dimensões espacial e temporal. Com o objetivo de modelar esses processos, Pfeifer e Deutsch (1980a) propuseram uma extensão da classe de modelos univariados de Box-Jenkins, denominada por modelo espaço-temporal autoregressivo de média móvel (STARMA). Essa classe de modelos é utilizada para descrever dados de séries temporais espacialmente localizadas. Os processos passíveis de modelagem via classe de modelos STARMA são caracterizados por observações de variáveis aleatórias, em que os locais a serem incorporados no modelo são fixos no espaço. A dependência entre as n séries temporais é modelada por meio da matriz de ponderação, de modo que os modelos da classe STARMA expressem cada observação no tempo t e na localização i como uma média ponderada de combinações lineares das observações anteriores e a inovação defasada no espaço e no tempo conjuntamente. Dada a nova classe de modelos, os objetivos deste estudo foram apresentar a classe de modelos STARMA, implentar computacionalmente, no software R, rotinas que permitam a análise de dados espaço-temporais, com as rotinas implementadas estabelecer e testar modelos de séries temporais aos dados de temperaturas mínimas médias mensais de 8 estações meteorológicas situadas no Paraná e comparar a classe de modelos STARMA com a classe de modelos univariados proposta por Box e Jenkins (1970). Com este estudo verificou-se que na apresentação da classe de modelos STARMA há complexidade no conceito de ordens de vizinhança e na identificação dos modelos espaço-temporais. Em relação a criação de rotinas responsáveis pelas análises de dados espaço-temporais observou-se dificuldades em sua implementação, principalmente no momento de estimação dos parâmetros. Na comparação da classe de modelos STARMA, multivariada, com a classe de modelos SARIMA, univariada, constatou-se que ambos os modelos se ajustaram de maneira satisfatória aos dados, produzindo previsões acuradas. / Spatio-temporal processes have been highlighted lately, due to the increase of studies approaching variables that present interactions between the spatial and temporal dimensions. In order to model these processes, Pfeifer e Deutsch (1980a) have suggested an extension of the Box-Jenkins univariate model class, named spatio-temporal autoregressive moving-average model (STARMA). This model class is used to describe spatially located time series data. The processes prone to be modeled via the STARMA model class are characterized by observations of random variables, in which the locations to be incorporated in the model are spatially fixed. The dependence between the n time series is modeled through the weighing matrix. So STARMA models express each observation at time t and location i as a weighed mean of linear combinations of the previous observations and the jointly lagged innovation in space and time. Given the new class models, the objectives of this study were to present a class of models STARMA, implentar computationally, in textit R software, routines that allow the analysis of spatio-temporal data with the routines implemented to establish and test models time series data of monthly average minimum temperatures of 8 meteorological stations located in Paraná and compare the class of models STARMA with the class of univariate models proposed by Box e Jenkins (1970). With this study it was found that the presentation of the class of models STARMA no complexity in the concept of ordered neighborhood and identification of spatio-temporal models. Regarding the creation of routines responsible for the analysis of spatio-temporal observed difficulties in its implementation, especially at the time of estimation of parameters. In comparison class STARMA models, multivariate, with the class of SARIMA models, univariate, it was found that both models were adjusted satisfactorily to the data, producing accurate forecasts.

Orientadores: André Franceschi de Angelis, Laura Letícia Ramos Rifo / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia / Made available in DSpace on 2018-08-20T08:56:00Z (GMT). No. of bitstreams: 1 Spagnol_RenataLussier_M.pdf: 2580788 bytes, checksum: a72f66f9e14fea6b229299437558a1ed (MD5) Previous issue date: 2011 / Resumo: A sociedade atual é dependente das Redes de Computadores para seu cotidiano e, portanto, mantê-las em boas condições de operação é essencial. Reagir aos problemas é uma estratégia que implica em degradação ou interrupção da rede e incorre geralmente em altos custos. é preferível detectar antecipadamente os problemas e corrigi-los proativamente, o que implica no uso de técnicas preditivas para controle, tais como os métodos estatísticos. Este trabalho determinou a possibilidade de se avaliar a rede com um menor número de variáveis em relação a um modelo existente e apontou maneiras de aprimorar a qualidade do monitoramento com uso técnicas estatísticas mais recentes e menos usuais. Os experimentos realizados consistiram-se na análise de traços de uma rede real previamente armazenados em bases de dados, sobre os quais foram aplicados cálculos de coeficiente de correlação linear para redução de variáveis. Ajustou-se um modelo para a rede com métodos de análises de Séries Temporais e foram testadas as cartas de Soma Acumulativa (CUSUM) e de Média Móvel Exponencialmente Ponderada (MMEP) em substituição às de média e amplitude. Obteve-se uma redução inicial de 23 para 4 na quantidade de variáveis a monitorar estatisticamente, com possibilidade de se chegar a uma única medida, simplificando os processos de controle da rede. Foi possível ajustar um Modelo Autoregressivo Integrado Média Móvel (ARIMA) para a rede e monitorá-la através de cartas CUSUM e MMEP, demonstrando-se a última mais adequada ao problema / Abstract: The nowadays society depends on computer networks for its daily activities and, therefore, it is essential to keep them in good operation conditions. React to the problems is a strategy that implies the network degradation or its interruption and increases maintenance costs. It is preferable the early detection of the problems and its proactive correction. This approach implies in the use of control prediction techniques, as stochastic methods. The present work has showed that the use of recent and less common statistics techniques can enhance the monitoring quality of the networks with fewer variables than a previous model. The linear correlation coefficient method was employed for the reduction of the number of variables over previously data base stored network traces. It was performed a model adjustment for the network using the temporal series method. The Cumulative Sum control chart (CUSUM) and the Exponentially Weighted Moving Average (EWMA) were used in replacement of common charts of average and range. It was obtained an initial reduction from 23 to 4 in the statistical monitored variables and it is possible to reach only one measure in some conditions, simplifying the network control process. It was possible to adjust an Autoregressive Integrated Moving Average (ARIMA) to the network and monitor it through CUSUM and EWMA. The last one was demonstrated to be the most suitable to the problem / Mestrado / Tecnologia e Inovação / Mestre em Tecnologia

Critical equipment plays an essential role in industry because of its lack of redundancy. Failure of critical equipment results in a major economic burden that will affect the profit of the enterprise. Lack of redundancy for critical equipment occurs because of the high cost of the equipment usually combined with its high reliability. <p><p>When we are analyzing the reliability of such equipment, as a result, there are few opportunities to crash a few pieces of equipment to actually verify component life. <p><p>Reliability is the probability that an item can perform its intended function for a specified interval of time under stated conditions and achieve low long-term cost of ownership for the system considering cost alternatives. From the economical standpoint, the overriding reliability issue is cost, particularly the cost of unreliability of existing equipment caused by failures. <p><p>Classical questions about reliability are:<p><p>·\ / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves only the subset of revelent variables. We propose two regularized stochastic regression approaches, for efficiently estimating certain sparse dependence structure in the data. We first consider a multivariate regression setting, in which the large number of responses and predictors may be associated through only a few channels/pathways and each of these associations may only involve a few responses and predictors. We propose a regularized reduced-rank regression approach, in which the model estimation and rank determination are conducted simultaneously and the resulting regularized estimator of the coefficient matrix admits a sparse singular value decomposition (SVD). Secondly, we consider model selection of subset autoregressive moving-average (ARMA) modelling, for which automatic selection methods do not directly apply because the innovation process is latent. We propose to identify the optimal subset ARMA model by fitting a penalized regression, e.g. adaptive Lasso, of the time series on its lags and the lags of the residuals from a long autoregression fitted to the time-series data, where the residuals serve as proxies for the innovations. Computation algorithms and regularization parameter selection methods for both proposed approaches are developed, and their properties are explored both theoretically and by simulation. Under mild regularity conditions, the proposed methods are shown to be selection consistent, asymptotically normal and enjoy the oracle properties. We apply the proposed approaches to several applications across disciplines including cancer genetics, ecology and macroeconomics.

The diploma thesis deals with econometric modelling and gold price forecast. A key factor is the multiple regression model and the ARIMA model. The first part of the diploma thesis contains a theoretical basis. The analytical part deals with modelling of gold market price and subsequent forecasting. Statistical and econometric verification using statistical methods play a very important role. The last part summarizes the results and makes suggestions for improvement.

This thesis deals with the failure mode detection of aircraft engines. The main approach to the detection is searching for anomalies in the sensor data. In order to get a comprehensive idea of the system and the particular sensors, the description of the whole system, namely the aircraft engine HTF7000 as well as the description of the sensors, are dealt with at the beginning of the thesis. A proposal of the anomaly detection algorithm based on three different detection methods is discussed in the second chapter. The above-mentioned methods are SVM (Support Vector Machine), K-means a ARIMA (Autoregressive Integrated Moving Average). The implementation of the algorithm including graphical user interface proposal are elaborated on in the next part of the thesis. Finally, statistical analysis of the results,the comparison of efficiency particular models and the discussion of outputs of the proposed algorithm can be found at the end of the thesis.

In this master thesis project, four well known time series forecasting models areconstructed and tuned with the purpose of predicting the future consumption of glueon one of AkzoNobels customers production lines. The goal was to examine thepossibility of utilizing their vastly collected data with these models to improve on theinventory management for both AkzoNobel and their customers. The predictedproduct usage rate would aid in the customers' decision making about when neworders of product should be placed, based on when the current storage tanks areforecasted to be emptied. This information could also be useful for AkzoNobelthemselves. The data that is handled in this project is a time series with timestampsfor every glue consumption process on the customers production line since 2017. Asubgoal was to determine what data resolution would be the most effective formodelling, so each model has two versions, one using higher and one using lowerresolution data. The models that are examined are a seasonal naive model,along-short term memory model, a Facebook Prophet model as well as two separateAutoregressive Integrated Moving Average models, specifically one automaticallyandone manually constructed. Beyond these models, a combined model using trueaveraging of the two automatic ARIMA models was examined as well.   Ultimately it was found that, for most models, forecasting ahead with a one day resolution was the most accurate using the models trained on one-day-separated-data, compared to three-hour-separated-data. Further it is presented that the best models are the two naive models, closely followed by the one-day-case automatic ARIMA and Prophet models. These models also performed similarly on simple tests for predicting a date when a tank will be empty. Mostly differing around four days on average from the true date for an empty tank on those tests, with a max forecast range of forty days. It is concluded that it is possible to sufficiently model the data to a point where the best models in this project could be an effective tool for both the AkzoNobel and its customers.

Theses deals with network traffic modeling focused on elaboration by time series analysis. The nature of network traffic is discussed above all http traffic. First three chapters are theoretical, which describes time series and basic models, linear AR, MA, ARMA, ARIMA and nonlinear ARCH. Other chapters define terms like self-similarity and long range dependence. It is demonstrated a failure of conventional models which cannot capture these specific properties of network data traffic. On the basis of study in chapter 6. is closely described the combined ARIMA/GARCH model and its parameter estimation procedure. Applied part of this theses deals with procedure of estimation and fitting the estimation model to observed network traffic. After an estimation a few future values are predicted on the basis of estimated model. These predicted values are consequently compared with real data.

Two real time series, one discussing the area of energy, other discussing the area of economy. By the energetic area we will be dealing with the electric power consumption in the USA, by the economic area we will be dealing with the progress of index PX50. We will try to approve the validity of hypothesis that with some test functions we will be able to set down the accidental unit distribution in these two time series.