The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

Um modelo estocástico combinado de previsão sazonal para a precipitação no Brasil

Este artigo discute um modelo de previsão combinada para a realização de prognósticos climáticos na escala sazonal. Nele, previsões pontuais de modelos estocásticos são agregadas para obter as melhores projeções no tempo. Utilizam-se modelos estocásticos autoregressivos integrados a médias móveis, de suavização exponencial e previsões por análise de correlações canônicas. O controle de qualidade das previsões é feito através da análise dos resíduos e da avaliação do percentual de redução da variância não-explicada da modelagem combinada em relação às previsões dos modelos individuais. Exemplos da aplicação desses conceitos em modelos desenvolvidos no Instituto Nacional de Meteorologia (INMET) mostram bons resultados e ilustram que as previsões do modelo combinado, superam na maior parte dos casos a de cada modelo componente, quando comparadas aos dados observados.

Impact of outdoor biomass air pollution on hypertension hospital admissions

Background This study aimed to evaluate the association between the total suspended particles (TSP) generated from burning sugar cane plantations and the incidence of hospital admissions from hypertension in the city of Araraquara. Methods The study was an ecological time-series study. Total daily records of hypertension (ICD 10th I10-15) were obtained from admitted patients of all ages in a hospital in Araraquara, Sao Paulo State, Brazil, from 23 March 2003 to 27 July 2004. The daily concentration of TSP (mu g/m(3)) was obtained using a Handi-Vol sampler placed in downtown Araraquara. The local airport provided daily measures of temperature and humidity. In generalised linear Poisson regression models, the daily number of hospital admissions for hypertension was considered to be the dependent variable and the daily TSP concentration the independent variable. Results TSP presented a lagged effect on hypertension admissions, which was first observed 1 day after a TSP increase and remained almost unchanged for the following 2 days. A 10 mu g/m(3) increase in the TSP 3 day moving average lagged in 1 day led to an increase in hypertension-related hospital admissions during the harvest period (12.5%, 95% CI 5.6% to 19.9%) that was almost 30% higher than during non-harvest periods (9.0%, 95% CI 4.0% to 14.3%). Conclusions Increases in TSP concentrations were associated with hypertension-related hospital admissions. Despite the benefits of reduced air pollution in urban cities achieved by using ethanol produced from sugar cane to power automobiles, areas where the sugar cane is produced and harvested were found to have increased public health risk.

The application of statistical process control charts to the detection and monitoring of hospital-acquired infections

The monitoring of infection control indicators including hospital-acquired infections is an established part of quality maintenance programmes in many health-care facilities. However, surveillance data use can be frustrated by the infrequent nature of many infections. Traditional methods of analysis often provide delayed identification of increasing infection occurrence, placing patients at preventable risk. The application of Shewhart, Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) statistical process control charts to the monitoring of indicator infections allows continuous real-time assessment. The Shewhart chart will detect large changes, while CUSUM and EWMA methods are more suited to recognition of small to moderate sustained change. When used together, Shewhart and EWMA methods are ideal for monitoring bacteraemia and multiresistant organism rates. Shewhart and CUSUM charts are suitable for surgical infection surveillance.

Mortalidade infantil no Brasil em períodos recentes de crise econômica

OBJETIVO: Analisar a tendência temporal da mortalidade infantil no Brasil em um período recente (1980 a 1998) de crise econômica. MÉTODOS: Estudo de série temporal tendo o Sistema de Informações de Mortalidade do Ministério da Saúde, Fundação IBGE e Fundação Nacional de Saúde como fontes de dados. Pela modelagem Autoregressive integrated moving average (ARIMA) descreveram-se parâmetros da série e, com coeficientes de correlação de Spearman, avaliou-se a associação entre coeficiente de mortalidade infantil e alguns determinantes. RESULTADOS A mortalidade infantil apresentou tendência decrescente (-59,3%) e forte correlação com a maioria dos indicadores analisados. Todavia, apenas as correlações entre coeficiente de mortalidade infantil e taxa de fecundidade total (e taxa de natalidade) diferiram significantemente de uma década para outra. CONCLUSÕES: A variação da fecundidade foi a principal responsável pela persistência do declínio da mortalidade infantil nos anos oitenta. No período seguinte, aqueles relacionados às condições de vida, principalmente, à atenção à saúde, talvez tenham sido mais importantes.

Análise dinâmica de mercados financeiros

Na sociedade actual, é cada vez mais difícil desassociar o ambiente financeiro do ambiente social, tendo o primeiro influência directa ou indirecta em praticamente todos os aspectos da sociedade. A esta influência está associada a vasta quantidade de informação e serviços financeiros que possibilitam uma melhor compreensão do ambiente socioeconómico actual, permitindo também o estudo das evoluções e das dinâmicas dos mercados financeiros. Este trabalho refere-se ao estudo e comparação de algumas ferramentas disponíveis para a análise dinâmica e tentativa de previsão de alguns índices de bolsa escolhidos. Tais métodos a estudar são modelos clássicos como o Autoregressivo, Média Móvel e o Modelo Misto apresentado por Box e Jenkins. São também propostos dois métodos que tentam distanciar-se dos métodos tradicionais por apenas considerarem para a sua previsão os momentos semelhantes ao momento actual que se tenta prever, ao invés de considerar todo o espectro dos dados disponíveis, tal como os métodos clássicos referidos anteriormente.

Análise técnica dos principais índices bolsistas europeus

Mestrado em Controlo e Gestão e dos Negócios

Seasonal variation in hospitalizations due to heart failure in Niterói city, Southeastern Brazil

The objective of the study was to describe seasonality of hospitalizations for heart failure in tropical climate as it has been described in cold climates. Seasonal Auto-regressive Integrated Moving-Average model was applied to time-series data of heart failure hospitalizations between 1996 and 2004 in Niteroi (Southeastern Brazil), collected from the Brazilian National Health Service Database. The standard seasonal variation was obtained by means of moving-average filtering and averaging data. The lowest and the highest annual hospital admissions were 507 (1997) and 849 (2002), respectively; the lowest and the highest monthly rates were 419 (December) and 681 (October), respectively. Peak admission rates were seen during the fall and winter. Although weak, the seasonality observed indicates that slight variations result in increased hospitalizations for heart failure.

Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

Improving accuracy for OTD based 3G geolocation in real urban/suburban environments

This paper presents the recent research results about the development of a Observed Time Difference (OTD) based geolocation algorithm based on network trace data, for a real Universal Mobile Telecommunication System (UMTS) Network. The initial results have been published in [1], the current paper focus on increasing the sample convergence rate, and introducing a new filtering approach based on a moving average spatial filter, to increase accuracy. Field tests have been carried out for two radio environments (urban and suburban) in the Lisbon area, Portugal. The new enhancements produced a geopositioning success rate of 47% and 31%, and a median accuracy of 151 m and 337 m, for the urban and suburban environments, respectively. The implemented filter produced a 16% and 20% increase on accuracy, when compared with the geopositioned raw data. The obtained results are rather promising in accuracy and geolocation success rate. OTD positioning smoothed by moving average spatial filtering reveals a strong approach for positioning trace extracted events, vital for boosting Self-Organizing Networks (SON) over a 3G network.

Weather parameters and nosocomial bloodstream infection: a case-referent study

OBJECTIVE To evaluate if temperature and humidity influenced the etiology of bloodstream infections in a hospital from 2005 to 2010.METHODS The study had a case-referent design. Individual cases of bloodstream infections caused by specific groups or pathogens were compared with several references. In the first analysis, average temperature and humidity values for the seven days preceding collection of blood cultures were compared with an overall “seven-days moving average” for the study period. The second analysis included only patients with bloodstream infections. Several logistic regression models were used to compare different pathogens and groups with respect to the immediate weather parameters, adjusting for demographics, time, and unit of admission.RESULTS Higher temperatures and humidity were related to the recovery of bacteria as a whole (versus fungi) and of gram-negative bacilli. In the multivariable models, temperature was positively associated with the recovery of gram-negative bacilli (OR = 1.14; 95%CI 1.10;1.19) or Acinetobacter baumannii (OR = 1.26; 95%CI 1.16;1.37), even after adjustment for demographic and admission data. An inverse association was identified for humidity.CONCLUSIONS The study documented the impact of temperature and humidity on the incidence and etiology of bloodstream infections. The results correspond with those from ecological studies, indicating a higher incidence of gram-negative bacilli during warm seasons. These findings should guide policies directed at preventing and controlling healthcare-associated infections.

A new least-squares approach to differintegration modeling

Signal Processing, Vol. 86, nº 10

Inserção de todos os consumidores no mercado liberalizado de energia elétrica: oportunidades e desafios

O processo de liberalização do setor elétrico em Portugal Continental seguiu uma metodologia idêntica à da maior parte dos países europeus, tendo a abertura de mercado sido efetuada de forma progressiva. Assim, no âmbito do acompanhamento do setor elétrico nacional, reveste-se de particular interesse caracterizar a evolução mais recente do mercado liberalizado, nomeadamente em relação ao preço da energia elétrica. A previsão do preço da energia elétrica é uma questão muito importante para todos os participantes do mercado de energia elétrica. Como se trata de um assunto de grande importância, a previsão do preço da energia elétrica tem sido alvo de diversos estudos e diversas metodologias têm sido propostas. Esta questão é abordada na presente dissertação recorrendo a técnicas de previsão, nomeadamente a métodos baseados no histórico da variável em estudo. As previsões são, segundo alguns especialistas, um dos inputs essenciais que os gestores desenvolvem para ajudar no processo de decisão. Virtualmente cada decisão relevante ao nível das operações depende de uma previsão. Para a realização do modelo de previsão de preço da energia elétrica foram utilizados os modelos Autorregressivos Integrados de Médias Móveis, Autoregressive / Integrated / Moving Average (ARIMA), que geram previsões através da informação contida na própria série temporal. Como se pretende avaliar a estrutura do preço da energia elétrica do mercado de energia, é importante identificar, deste conjunto de variáveis, quais as que estão mais relacionados com o preço. Neste sentido, é realizada em paralelo uma análise exploratória, através da correlação entre o preço da energia elétrica e outras variáveis de estudo, utilizando para esse efeito o coeficiente de correlação de Pearson. O coeficiente de correlação de Pearson é uma medida do grau e da direção de relação linear entre duas variáveis quantitativas. O modelo desenvolvido foi aplicado tendo por base o histórico de preço da eletricidade desde o inicio do mercado liberalizado e de modo a obter as previsões diária, mensal e anual do preço da eletricidade. A metodologia desenvolvida demonstrou ser eficiente na obtenção das soluções e ser suficientemente rápida para prever o valor do preço da energia elétrica em poucos segundos, servindo de apoio à decisão em ambiente de mercado.

Studies on entomological monitoring: mosquito species frequency in riverine habitats of the Igarapava Dam, Southern Region, Brazil

Diversity of mosquito species was evaluated in different habitats before and after the Igarapava reservoir flooding in the Grande River, Southern Cerrado of Brazil. We aimed at verifying changes in these mosquito populations in consequence of the lake formation. Four habitats were selected as sampling stations: peridomiciliary habitat, pasture, "veredas" and gallery forest patch. Bimonthly collections were made with the Shannon trap and human bait, including diurnal, crepuscular and nocturnal period of mosquito activity. The Shannon Index results from the potential vectors were compared using Student t-test. Aedes scapularis, Anopheles darlingi and An. albitarsis senso latu seasonal abundance were described with moving average and compared using chi2 test. There were changes in the mosquito frequency in the habitats, except for the "veredas" that was 13 km away from the catchment area. The altering in mosquito species seasonal abundance suggests breeding places expansion. Diversity indexes can be used to monitor changes in mosquito vector population in environments where abrupt disturbance can alter disease transmission cycles.