999 resultados para ARIMA Model


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Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

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Time series tendencies are an important tool for different sectors such as the scientific community, industries and environmental protection agencies who can evaluate the variability of a specific parameter in time, what is very important piece of information for establishing corrective and preventive actions. This work presents a time series model of main physical, chemical and biological parameters of the Water Quality Index (WQI) determined for different selected points of a hydrographical basin form May/2006 to Aug/2010. The statistical model Arima enabled a better understanding of the physical, chemical and biological processes that most clearly influences WQI. The Arima model allowed the assessment of the trend of several parameters used in the calculation of the WQI, showing that dissolved oxygen, turbidity, total nitrogen, and fecal E. coli were highly correlated and are the parameters that caused the index changes over time.

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Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientadora: Professora Doutora Patrícia Ramos

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This study aimed to investigate the behaviour of two indicators of influenza activity in the area of Barcelona and to evaluate the usefulness of modelling them to improve the detection of influenza epidemics. DESIGN: Descriptive time series study using the number of deaths due to all causes registered by funeral services and reported cases of influenza-like illness. The study concentrated on five influenza seasons, from week 45 of 1988 to week 44 of 1993. The weekly number of deaths and cases of influenza-like illness registered were processed using identification of a time series ARIMA model. SETTING: Six large towns in the Barcelona province which have more than 60,000 inhabitants and funeral services in all of them. MAIN RESULTS: For mortality, the proposed model was an autoregressive one of order 2 (ARIMA (2,0,0)) and for morbidity it was one of order 3 (ARIMA (3,0,0)). Finally, the two time series were analysed together to facilitate the detection of possible implications between them. The joint study of the two series shows that the mortality series can be modelled separately from the reported morbidity series, but the morbidity series is influenced as much by the number of previous cases of influenza reported as by the previous mortality registered. CONCLUSIONS: The model based on general mortality is useful for detecting epidemic activity of influenza. However, because there is not an absolute gold standard that allows definition of the beginning of the epidemic, the final decision of when it is considered an epidemic and control measures recommended should be taken after evaluating all the indicators included in the influenza surveillance programme.

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The objective of the present study was to characterize the heart rate (HR) patterns of healthy males using the autoregressive integrated moving average (ARIMA) model over a power range assumed to correspond to the anaerobic threshold (AT) during discontinuous dynamic exercise tests (DDET). Nine young (22.3 ± 1.57 years) and 9 middle-aged (MA) volunteers (43.2 ± 3.53 years) performed three DDET on a cycle ergometer. Protocol I: DDET in steps with progressive power increases of 10 W; protocol II: DDET using the same power values as protocol 1, but applied randomly; protocol III: continuous dynamic exercise protocol with ventilatory and metabolic measurements (10 W/min ramp power), for the measurement of ventilatory AT. HR was recorded and stored beat-to-beat during DDET, and analyzed using the ARIMA (protocols I and II). The DDET experiments showed that the median physical exercise workloads at which AT occurred were similar for protocols I and II, i.e., AT occurred between 75 W (116 bpm) and 85 W (116 bpm) for the young group and between 60 W (96 bpm) and 75 W (107 bpm) for group MA in protocols I and II, respectively; in two MA volunteers the ventilatory AT occurred at 90 W (108 bpm) and 95 W (111 bpm). This corresponded to the same power values of the positive trend in HR responses. The change in HR response using ARIMA models at submaximal dynamic exercise powers proved to be a promising approach for detecting AT in normal volunteers.

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This paper examined the transmission mechanism of international prices of agricultural commodities into the real exchange rate in Brazil for the period from January 2000 to February 2010. We used time series models (ARIMA Model, Transfer Model, Intervention Analysis, Johansen Cointegration Test) in determination of the short and long run elasticities. Transfer Function Model results show that changes in international prices of agricultural commodities are transmitted to the real exchange rate in Brazil in the short run, however, that transmission is less than unity, thus configuring the inelastic relationship. Johansen cointegration tests show that these variables are not co-integrated, no longer converge to the long-run equilibrium. These results are in agreement Cashim et al. (2004), which also found no long run relationship between real exchange rate and commodity prices in the case of Brazil. These results show that monetary shocks have greater weight on changes of the real exchange rate than real shocks.

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Este trabalho investiga e analisa as diferenças das taxas anuais de inflação realizadas com relação às previsões dos agentes econômicos do mercado para um ano à frente. Os índices analisados foram o IPCA, IPA-M, IGP-M e o IGP-DI. Referente à previsão dos agentes para cada índice, foi feito uma análise estatística e uma análise de séries temporais através do modelo ARIMA. Este último explicou o erro de previsão dos agentes econômicos através de valores passados, ou defasados, do próprio erro de previsão, além dos termos estocásticos.

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Includes bibliography

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Introdução: Infecções relacionadas à assistência de saúde (IRAS) representam hoje um dos principais desafios da qualidade do cuidado do paciente, principalmente em pacientes submetido a transplante de células tronco e hematopoiéticas (TCTH) O banho diário com a clorexidina (CHG) degermante a 2% tem sido proposto principalmente em unidades de terapia intensivas (UTIs) para diminuir a colonização bacteriana do paciente e assim diminuir IRAS. O objetivo deste estudo foi avaliar o impacto do banho com CHG degermante a 2% em unidade de internação de TCTH na incidência de infecção e colonização por patógenos multirresistentes e ainda avaliar seu impacto na sensibilidade das bactérias ao antisséptico. Métodos: Foi realizado um estudo quasi-experimental, com duração de 9 anos, com início em janeiro/2005 até dezembro/2013. A intervenção foi iniciada em agosto de 2009, sendo que os períodos pré e pós-intervenção tiveram duração de 4,5 anos. As taxas de IRAS, infecção por gram-negativos multirresistentes e infecção e colonização por enterococo resistente a vancomicina (VRE) foram avaliadas através de série temporal, para estudar o impacto da intervenção. As concentrações inibitórias mínimas (CIM) das bactérias para a CHG com e sem o inibidor de bomba de efluxo (CCCP) foram avaliadas nos dois períodos. Os genes de resistência a CHG foram estudados por meio da PCR e a clonalidade dos isolados por eletroforese em campo pulsátil. Resultados: Foi observada redução significativa na incidência de infecção e colonização de VRE na unidade no período pós-intervenção (p: 0,001). Essa taxa permaneceu estável em outras UTIs clínicas do hospital. Contudo as taxas de infecção por Gram negativos multirresistentes aumentou nos últimos anos na unidade. Não ocorreu diminuição na taxa de IRAS na unidade. As CIMs testadas de CHG aumentaram nas amostras de VRE e K. pneumoniae após o período de exposição ao antisséptico, com queda importante da CIM após o uso do CCCP, revelando ser a bomba de efluxo, um importante mecanismo de resistência à CHG. As amostras de A. baumannii e P. aeruginosa não apresentaram aumento da CIM após período de exposição à clorexidina. As bombas de efluxo Ade A, B e C estiveram presentes na maioria dos A. baumannii do grupo controle (66%). A bomba cepA foi encontrada em 67% de todas as K. pneumoniae testadas e em 44,5% das P. aeruginosas do grupo pré intervenção. Observamos uma relação positiva entre a presença da CepA nas amostras de K. pneumoniae e a resposta ao CCCP: de todas as 49 amostras CepA positivas 67,3% obtiveram redução do seu MIC em 4 diluições após adição do CCCP. A avaliação de clonalidade demonstrou padrão policlonal das amostras de VRE, K. pneumoniae e A. baumannii avaliadas. Em relação às amostras de P. aeruginosa foi observado que no período pós-intervenção ocorreu predominância de um clone com > 80% semelhança em 10 das 22 amostras avaliadas pelo dendrograma. Conclusões: O banho de clorexidina teve impacto na redução da incidência de infecção e colonização por VRE na unidade de TCTH, e não teve o mesmo impacto nas bactérias gram-negativas. Os mecanismos moleculares de resistência à clorexidina estão intimamente ligados à presença de bomba de efluxo, sendo provavelmente o principal mecanismo de resistência e tolerância das bactérias ao antisséptico

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Thesis (Master's)--University of Washington, 2016-06

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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.

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This thesis attempts to find the least-cost strategy to reduce CO2 emission by replacing coal by other energy sources for electricity generation in the context of the proposed EPA’s regulation on CO2 emissions from existing coal-fired power plants. An ARIMA model is built to forecast coal consumption for electricity generation and its CO2 emissions in Michigan from 2016 to 2020. CO2 emission reduction costs are calculated under three emission reduction scenarios- reduction to 17%, 30% and 50% below the 2005 emission level. The impacts of Production Tax Credit (PTC) and the intermittency of renewable energy are also discussed. The results indicate that in most cases natural gas will be the best alternative to coal for electricity generation to realize CO2 reduction goals; if the PTC for wind power will continue after 2015, a natural gas and wind combination approach could be the best strategy based on the least-cost criterion.

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Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

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The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.