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Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

Odan, Frederico Keizo; Ribeiro Reis, Luisa Fernanda
Fonte: ASCE-AMER SOC CIVIL ENGINEERS; RESTON Publicador: ASCE-AMER SOC CIVIL ENGINEERS; RESTON
Tipo: Artigo de Revista Científica
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This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models...

Previsão de demanda de água na Região Metropolitana de São Paulo com redes neurais e artificiais e condições sócio-ambientais e meteorológicas.; Water demand forecasting in the metropolitan area São Paulo with Artificial Neural Network and socioenvironmental and meteorological conditions.

Santos, Cláudia Cristina dos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 17/05/2011 Português
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O presente trabalho apresenta a previsão de demanda de água em sistemas urbanos de abastecimento através de Rede Neural Artificial (RNA) utilizando dados de consumo de água e variáveis meteorológicas e socioambientais. A RNA utilizada foi uma de três camadas chamada de rede de múltiplas camadas alimentadas adiante com o algoritmo de treinamento LLSSIM (Hsu et al., 1996). Neste estudo, foram utilizados os dados de consumo de água (SABESP) e meteorológicos (IAG/USP) para o período de 2001 a 2005 para Região Metropolitana de São Paulo (RMSP). As variáveis socioambientais e meteorológicas que podem afetar o consumo de água foram analisadas. A ETA Cantareira e o setor Itaim Paulista foram utilizados para avaliar a relação entre o consumo e as variáveis antrópicas e meteorológicas para o ano de 2005. Esses conjuntos de dados foram utilizados para o treinamento, o teste e a previsão da RNA. Para a ETA Cantareira, foram criados 8 modelos e para o setor Itaim Paulista 57, sendo que os modelos 9 a 57 correspondem à previsão ideal. O desempenho dos modelos foi avaliado pelo o erro médio, erro médio absoluto, erro médio quadrático, o coeficiente de correlação, exatidão, viés, POD, FAR, CSI e POFD. Para a ETA Cantareira o melhor desempenho ocorreu para a média de 12 horas e para o Itaim Paulista a média de 6 horas. Na previsão ideal observou-se que a memória do sistema é um fator importante...

Previsão do volume diário de atendimentos no serviço de pronto socorro de um hospital geral: comparação de diferentes métodos; Forecasting daily emergency department visits using calendar variables and ambient temperature readings: comparison of different models applied to a setting in Sao Paulo - Brazil

Souza, Izabel Oliva Marcilio de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 11/09/2013 Português
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OBJETIVOS: O estudo explorou diferentes métodos de séries temporais visando desenvolver um modelo para a previsão do volume diário de pacientes no Pronto Socorro do Instituto Central do Hospital das Clínicas da Faculdade de Medicina da USP. MÉTODOS: Foram explorados seis diferentes modelos para previsão do número diário de pacientes no pronto socorro de acordo com algumas variáveis relacionadas ao calendário e à temperatura média diária. Para a construção dos modelos, utilizou-se a contagem diária de pacientes atendidos no pronto socorro entre 1° de janeiro de 2008 a 31 de dezembro de 2010. Os primeiros 33 meses do banco de dados foram utilizados para o desenvolvimento e ajuste dos modelos, e os últimos três meses foram utilizados para comparação dos resultados obtidos em termos da acurácia de previsão. A acurácia foi medida a partir do erro médio percentual absoluto. Os modelos foram desenvolvidos utilizando-se três diferentes métodos: modelos lineares generalizados, equações de estimação generalizadas e modelos sazonais autorregressivos integrados de média móvel (SARIMA). Para cada método, foram testados modelos que incluíram termos para controlar o efeito da temperatura média diária e modelos que não incluíram esse controle. RESULTADOS: Foram atendidos...

Fractais e redes neurais artificiais aplicados à previsão de retorno de ativos financeiros brasileiros; Fractals and artificial neural networks applied to return forecasting of Brazilian financial assets

Mendonça Neto, João Nunes de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 13/08/2014 Português
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Este estudo tem como problema de pesquisa a previsão de retorno de ativos financeiros. Buscou verificar a existência de relação entre memória ou dependência de longo prazo em séries temporais fractais e erro de previsão de retornos de ativos financeiros obtida por meio de Redes Neurais Artificiais (RNA). Espera-se que séries temporais fractais com maior memória de longo prazo permitam obter previsões com menor nível de erro, na medida em que a correlação entre os elementos da série favoreça a qualidade de previsão de RNA. Como medida de memória de longo prazo, foi calculado o expoente de Hurst de cada série temporal, o qual sofreu uma transformação para atuar como um índice de previsibilidade. Para medir o erro de previsão, foi utilizada a Raiz do Erro Quadrado Médio (REQM) produzida pela RNA em cada série temporal. O cálculo do expoente de Hurst foi realizado por meio do algoritmo da análise Rescaled Range (R/S). A arquitetura de RNA utilizada foi a de Rede Neural com Atraso Alimentada Adiante (TLFN), tendo como processo de aprendizagem supervisionada o modelo de retropropagação com gradiente descendente para minimização do erro. A amostra foi composta por ativos financeiros brasileiros negociados na Bolsa de Valores...

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Melo, Joel D.; Carreno, Edgar Manuel; Padilha-Feltrin, Antonio
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 1870-1878
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting techniques that usually consider the service zone as a static entity to model or simulate the spatial electric load forecasting in a city. This paper aims to determine how the electric load will be distributed among the sub-zones in the city. For this, the service zone is divided into several subzones, each subzone considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented through development probabilities. These probabilities are considered as input data for the simulation. Given this setting, different kinds of agents can be developed to simulate the growth pattern of the loads in distribution systems in parallel. The approach is tested with data from a real distribution system in a mid-size city; the results show a low spatial error when compared to real data. Less than 6% of the load growth was identified 0.71 km outside of its correct location on the test system.

Towards forecasting and mitigating ionospheric scintillation effects on GNSS

Aquino, M.; Dodson, A.; DeFranceschi, G.; Alfonsi, L.; Romano, V.; Monico, J. F G; Marques, H.; Mitchell, C.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 63-67
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The effect of the ionosphere on the signals of Global Navigation Satellite Systems (GNSS), such as the Global Positionig System (GPS) and the proposed European Galileo, is dependent on the ionospheric electron density, given by its Total Electron Content (TEC). Ionospheric time-varying density irregularities may cause scintillations, which are fluctuations in phase and amplitude of the signals. Scintillations occur more often at equatorial and high latitudes. They can degrade navigation and positioning accuracy and may cause loss of signal tracking, disrupting safety-critical applications, such as marine navigation and civil aviation. This paper addresses the results of initial research carried out on two fronts that are relevant to GNSS users if they are to counter ionospheric scintillations, i.e. forecasting and mitigating their effects. On the forecasting front, the dynamics of scintillation occurrence were analysed during the severe ionospheric storm that took place on the evening of 30 October 2003, using data from a network of GPS Ionospheric Scintillation and TEC Monitor (GISTM) receivers set up in Northern Europe. Previous results [1] indicated that GPS scintillations in that region can originate from ionospheric plasma structures from the American sector. In this paper we describe experiments that enabled confirmation of those findings. On the mitigation front we used the variance of the output error of the GPS receiver DLL (Delay Locked Loop) to modify the least squares stochastic model applied by an ordinary receiver to compute position. This error was modelled according to [2]...

Top-down or bottom-up forecasting?

Wanke,Peter; Saliby,Eduardo
Fonte: Sociedade Brasileira de Pesquisa Operacional Publicador: Sociedade Brasileira de Pesquisa Operacional
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2007 Português
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The operations literature continues on inconclusive as to the most appropriate sales forecasting approach (Top-Down or Bottom-up) for the determination of safety inventory levels. This paper presents the analytical results for the variance of the sales forecasting errors during the lead-time in both approaches. The forecasting method used was the Simple Exponential Smoothing and the results led to the identification of two supplementary impacts upon the forecasting error variance, and consequently, upon safety inventory levels: the Portfolio Effect and the Anchoring Effect. The first depends upon the correlation coefficient of demand between two individual items and the latter, depends upon the smoothing constant and upon the participation of the individual item in total sales. It is also analysed under which conditions these variables would favour one forecasting approach instead of the other.

Demand Forecasting Errors

Mackie, Peter; Nellthorp, John; Laird, James
Fonte: World Bank, Washington, DC Publicador: World Bank, Washington, DC
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Demand forecasts form a key input to the economic appraisal. As such any errors present within the demand forecasts will undermine the reliability of the economic appraisal. The minimization of demand forecasting errors is therefore important in the delivery of a robust appraisal. This issue is addressed in this note by introducing the key issues, and error types present within demand forecasts (Section 1). Following that introductory section the error types are described in more detail: measurement error (Section 2), model specification error (Section 3) and External or Exogenous Errors (Section 4). The final section presents a discussion on how to manage demand forecasting errors (Section 5).

Mitigating Congestion by Integrating Time Forecasting and Realtime Information Aggregation in Cellular Networks

Chen, Kai
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
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An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban...

Uncertainties in flood forecasting: A Bayesian total error perspective

Kavetski, D.; Renard, B.; Evin, G.; Thyer, M.; Newman, A.; Kuczera, G.
Fonte: Bureau of Meteorology and CSIRO; Australia Publicador: Bureau of Meteorology and CSIRO; Australia
Tipo: Conference paper
Publicado em //2011 Português
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In flood forecasting problems where the streamflow response to rainfall is relatively quick, two steps are typically necessary: i) update the rainfall–runoff model and ideally, the probability models that describe errors; and ii) forecast future streamflow using rainfall forecasts. Step ii is a forward propagation of uncertainty, which is contingent on an adequate rainfall forecasting system. On the other hand, Step i represents an inverse problem posing several well‑known challenges that are difficult to resolve. For example, the errors in rainfall and streamflow observations are complex and are poorly approximated in traditional Kalman filter‑type schemes. This paper focuses on Step i. Starting with a probabilistic description of the uncertainties in model inputs, outputs and structure, a Bayesian total error analysis framework is formulated to improve the reliability and precision of forecasting systems. An important feature of this framework is that it infers the parameters of both the rainfall–runoff model and of the error models describing rainfall, streamflow and model uncertainty. This enables ‘training’ of the error models by the data and can improve the reliability of the predictions. However, independent (prior) information is required to ensure well‑posedness.; http://www.csiro.au/Portals/Publications/WIRADA_WfHC_Pub.aspx; Kavetski D...

Forecasting with Factor-augmented Error Correction Models

MASTEN, Igor; BANERJEE, Anindya; MARCELLINO, Massimiliano
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
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As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.

Does the Box-Cox Transformation Help in Forecasting Macroeconomic Time Series?

PROIETTI, Tommaso; LUETKEPOHL, Helmut
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
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The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-step-ahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the na¨ive predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance.

Forecasting with factor-augmented error correction models

BANERJEE, Anindya; MARCELLINO, Massimiliano; MASTEN, Igor
Fonte: Elsevier Science Bv Publicador: Elsevier Science Bv
Tipo: Artigo de Revista Científica
Português
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As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.; This article is based on EUI RSCAS WP 2009/32

A comparison and evaluation of performances among crop yield forecasting models based on remote sensing: Results from the GEOLAND Observatory of Food Monitoring

FRITZ Steffen; GENOVESE GIAMPIERO; BETTIO MANOLA
Fonte: Internationsl Society for Photogrammetry and Remote Sensinsing (ISPRS) Publicador: Internationsl Society for Photogrammetry and Remote Sensinsing (ISPRS)
Tipo: Contributions to Conferences Formato: Printed
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In the context of the GEOLAND EC FP6 project the comparison of different remote sensing based approaches for yield forecasting over large areas in Europe are tested and results inter-compared. In particular the methods tested include the ones in use within the MARS-Crop Yield Forecasting System as the results from a Crop Growth Monitoring model (Alterra) and vegetation indicators derived from Low Resolution VGT and NOAA Images (VITO, IGiK), METEOSAT based yield forecasting (EARS) and ERS-Scatterometer Crop Performance Index (TPF and NEO). Performances of the different models were tested in Spain, Belgium and Poland. The inter-comparison of the crop yield forecasts were mainly based on the forecasting error obtained from the different approaches based on the Root Mean Square Forecast Error (RMSFE). This error was derived by comparing the predicted yields of the different models with the official yield from EUROSTAT. The comparison of the RMSFE was used to verify the convergence of results from the different models, the reliability of the information, i.e. precision and bias, and its precocity compared to the crop cycle. The results showed that the indicators are able to give reliable information with some differences: remote sensing indicators are more precise and accurate in southern areas (less cloud cover) while in northern areas good results are obtained under the use of better local calibrations of traditional crop yield forecasting systems...

A Comparison and Evaluation of Performances among Crop Yield Forecasting Models Based on Remote Sensing: Results from Tte Geoland Observatory of Food Monitoring

GENOVESE GIAMPIERO; FRITZ Stephen; BETTIO MANOLA
Fonte: International Society for Photogrammetry and Remote Sensing (ISPRS) Publicador: International Society for Photogrammetry and Remote Sensing (ISPRS)
Tipo: Contributions to Conferences Formato: CD-ROM
Português
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38.04082%
In the context of the GEOLAND EC FP6 project the comparison of different remote sensing based approaches for yield forecasting over large areas in Europe are tested and results inter-compared. In particular the methods tested include the ones in use within the MARS-Crop Yield Forecasting System as the results from the Crop Growth Monitoring System model and vegetation indicators derived from Low Resolution SPOT-VGT and NOAA Images, METEOSAT based yield forecasting and ERS-Scatterometer Crop Performance Index. Performances of the different models were tested in Spain, Belgium and Poland. The inter-comparisons of the crop yield forecasts were mainly based on the forecasting error obtained from the different approaches based on the Root Mean Square Forecast Error (RMSFE). This error was derived by comparing the predicted yields of the different models with the official yield as from official statistics (EUROSTAT). The comparison of the RMSFE was used to verify the convergence of results from the different models, the reliability of the information, i.e. precision and bias, and its precocity compared to the crop cycle. The results showed that the indicators are able to give reliable information with some differences: remote sensing indicators are more precise and accurate in southern areas (less cloud cover) while in northern areas good results are obtained under the use of better local calibrations of traditional crop yield forecasting systems and/or the use of additional information for instance remote sensing data as inputs into advanced crop modelling systems. Furthermore...

Government imposed constraints and forecasting analysis on the M.J. Soffe Corporation

Donahue, Kimberly A.; Parsons, Joshua M.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Formato: xii, 61 p. : ill. (some col.) ; 28 cm.
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MBA Professional Report; Approved for public release; distribution in unlimited.; The purpose of this project is to evaluate the impact of the federal requirements process on the Military Sales Division of the M.J. Soffe Corporation (Soffe), apparel manufacturer, and to identify areas of influence that Soffe can control to shape the requirements of future military needs. M.J. Soffe is a main government supplier of the U.S. Marine Corps uniform olive drab and brown crew neck undershirts. This is a study that complements M.J. Soffe's effort to understand the Federal Acquisition Regulation (FAR) and requirements process to improve their efficiency for future growth. This project will look at the external environment which influences the military garment industry. Also, an analysis of the requirements generation process will be completed to provide recommended opportunities for M.J. Soffe to shape future apparel requests of the military services. Furthermore, the identification of government constraints has effects on manufacturing and sales planning. This report will also look at the residual implications of this relationship towards forecast error and inventory levels.; Captain, United States Marine Corps; Captain, United States Air Force

Forecasting Marine Corps enlisted manpower inventory levels with univariate time series models

Feiring, Douglas I.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xvi, 89 p. : ill. ;
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Accurately forecasting future personnel inventory levels by rank and occupational specialty is a fundamental prerequisite for development of an effective and functional staffing plan. This thesis develops and evaluates univariate time series models to create six- and twelve-month forecasts of Marine Corps enlisted manpower levels. Models are developed for 44 representative population groups using Holt-Winters exponential smoothing, multiplicative decomposition, and Box-Jenkins autoregressive integrated moving average (ARIMA) forecasting methods. The forecasts are evaluated against actual, out-of-sample inventory levels using several goodness-of-fit indicators including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Sum of Squared Errors (SSE). Among the modeling techniques evaluated, the multiplicative decomposition performed the best overall and represents an improvement over the Marine Corps' current forecasting method. This thesis recommends Marine Corps Systems Command, Total Force Information Technology Systems develop and introduce a multiplicative decomposition forecasting model into the Enlisted Staffing Goal Model. This forecasting technique should be implemented in phases, starting with the E-1 through E-4 population groups.

Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river

Bowden, G.; Maier, H.; Dandy, G.
Fonte: Elsevier Science BV Publicador: Elsevier Science BV
Tipo: Artigo de Revista Científica
Publicado em //2005 Português
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This paper is the second of a two-part series in this issue that presents a methodology for determining an appropriate set of model inputs for artificial neural network (ANN) models in hydrologic applications. The first paper presented two input determination methods. The first method utilises a measure of dependence known as the partial mutual information (PMI) criterion to select significant model inputs. The second method utilises a self-organising map (SOM) to remove redundant input variables, and a hybrid genetic algorithm (GA) and general regression neural network (GRNN) to select the inputs that have a significant influence on the model's forecast. In the first paper, both methods were applied to synthetic data sets and were shown to lead to a set of appropriate ANN model inputs. To verify the proposed techniques, it is important that they are applied to a real-world case study. In this paper, the PMI algorithm and the SOM–GAGRNN are used to find suitable inputs to an ANN model for forecasting salinity in the River Murray at Murray Bridge, South Australia. The proposed methods are also compared with two methods used in previous studies, for the same case study. The two proposed methods were found to lead to more parsimonious models with a lower forecasting error than the models developed using the methods from previous studies. To verify the robustness of each of the ANNs developed using the proposed methodology...

Forecasting Hospital Emergency Department Visits for Respiratory Illness Using Ontario's Telehealth System: An Application of Real-Time Syndromic Surveillance to Forecasting Health Services Demand

PERRY, ALEXANDER
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 8189225 bytes; application/pdf
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Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID...

Forecasting the spot prices of various coffee types using linear and non-linear error correction models

Milas, Costas; Otero Cardona, Jesús Gilberto; Panagiotidis, Theodore
Fonte: Facultad de Economía Publicador: Facultad de Economía
Tipo: info:eu-repo/semantics/book; info:eu-repo/semantics/acceptedVersion Formato: application/pdf
Publicado em //2001 Português
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Este documento estima modelos lineales y no-lineales de corrección de errores para los precios spot de cuatro tipos de café. En concordancia con las leyes económicas, se encuentra evidencia que cuando los precios están por encima de su nivel de equilibrio, retornan a éste mas lentamente que cuando están por debajo. Esto puede reflejar el hecho que, en el corto plazo, para los países productores de café es mas fácil restringir la oferta para incrementar precios, que incrementarla para reducirlos. Además, se encuentra evidencia que el ajuste es más rápido cuando las desviaciones del equilibrio son mayores. Los pronósticos que se obtienen a partir de los modelos de corrección de errores no lineales y asimétricos considerados en el trabajo, ofrecen una leve mejoría cuando se comparan con los pronósticos que resultan de un modelo de paseo aleatorio.; This paper estimates linear and non-linear error correction models for the spot prices of four different coffee types. In line with economic priors, we find some evidence that when prices are too high, they move back to equilibrium more slowly than when they are too low. This may reflect the fact that, in the short run, it is easier for countries to restrict the supply of coffee in order to raise prices...