Tillverkning mot kundorder kräver oftast att hålla ett förrådslager med råmaterial. Ökade kundkrav på korta leveranstider kräver att material skall finnas tillgängligt i lager. Detta ger upphov till ökade lager hos ett företag som i sin tur gör att de binder mer kapital och samtidigt leder till en sämre av­kastning. För att undvika att hålla för stora förrådslager men samtidigt hålla en god servicenivå krävs en effektiv lagerstyrning. Syftet med detta projekt är att ta reda på hur efterfrågan ser ut hos företaget i dagsläget samt kunna ta fram en lämplig prognosmetod och beräkningsmodell mot en viss takt för lagerstyrningen. Prognos­metoderna skall vara ett underlag för inköp vid beställning av material. Detta skall ligga till grund för att standardisera arbetssättet och underlätta exempelvis arbetet för nyanställda. Syftet med arbetet är också att med hjälp av en prognosmetod kunna hålla kapitalbindningen av råmateriallagret på en lämplig nivå. De metoderna som behandlas i detta arbete är ABC-analys, be­räkning av säkerhetslager och prognosmetoder. Resultatet från detta arbete skall besvara målen som projektet strävar efter. Detta skall genom olika analyser och experiment undersöka vilken typ av prognosmetod som företaget bör använda. Metoderna och teorierna som tas upp i rapporten har även syftet att kunna användas av liknande företag. Den valda prognosmetoden skall även resultera i ett bättre underlag för att komma närmre den verkliga efterfrågan som är en del för att uppnå en förbättrad lagerstyrning. Detta har i största möjliga mån satts i relation till det arbetssätt som används på företaget idag. En del av arbetet har gått ut på att ta reda på hur mycket efterfrågan varierarat från beställning tills materialet står i råmaterialslagret. Detta har även använts för att jämföra prognosmetoderna mot det nuvarande arbetssättet. Eftersom företaget vill expandera sin produktion har även en beräkningsmodell tagits fram. Denna modell anger lagernivån inklusive säkerhetslager mot en önskad takt. / Production to customer order usually requires keeping a supply warehouse. Increased customer demands for short lead-times require that materials must be in stock. This result in increasing stock levels in a company leads to more capital tied up and simultaneously leads to poorer yields. To avoid keeping a large storage warehouse whilst keeping a good service requires an effective inventory control. The purpose of this project is to find out how the demand looks like in the current situation of the company and to develop an appropriate forecasting method and calculation model against a certain pace for inventory control. The methods mentioned in this work are the ABC analysis, calculation of safety stock and forecasting methods. The results from this work will answer the objectives of the project aims. A variety of analyzes and experiments shall be used to investigate what type of forecasting method that the company should use. The methods and theories raised in the report also aims to be used by similar companies. The aim of the selected forecasting method is also to lead to a better base, from which better forecast precision is one part in order to improve the inventory management. This has, as far as possible, been put in relation to the working methods used in the company today. Some of the work has been to find out how much demand changes during the lead times, i.e. from the ordering of raw material until the material is in the raw material stock. This has also been used to compare forecasting methods against the present approach. Because the company wants to expand its production, a calculation model has also been developed. This model indicates the inventory level to a desired pace, i.e. demand level.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

This dissertation collects five independent essays. The first essay is An Alternative Explanation of the Price Puzzle. The most widely accepted explanation of the price puzzle points to an inadequate performance of the VAR in forecasting inflation. This essay suggests that the finding of a price puzzle is due to a seemingly innocent misspecification in taking the theoretical model to the data: a measure of output gap is not included in the VAR (output alone being used instead), while this variable is a crucial element in every equation of the theoretical models. When the VAR is correctly specified, the price puzzle disappears. Building on results contained in the first paper, the second-- Stronger Evidence of Long-Run Neutrality: A comment on Bernanke and Mihov---improves the empirical performance of standard models on the prediction that a monetary policy shock should have temporary effects on output. It turns out that the same misspecification causing the price puzzle is also responsible for overestimation of the time needed for the effects on output of a monetary policy shock to die out. The point can be proven in a theoretical economy, and is confirmed on US data. Monetary Policy Without Monetary Aggregates: Some (Surprising) Evidence , joint with Giovanni Favara) is the third essay. It points to what seems to be a falsified prediction of models in the New-Keynesian framework. In this framework monetary aggregates are reserved a pretty boring role, so boring that they can be safely excluded from the final lay out of the model. These models predict that a money demand shock should have no effect on output, inflation and interest rate. However, the prediction seems to be quite wrong Inflation Forecast Targeting, joint with Paul Söderlind, takes a step outside the representative-agent framework. In RE models, all agents typically have the same information set, and therefore make the same predictions. However, in the real even professional forecasters show substantial disagreement. This disagreement can have an impact on asset prices and transaction volumes, among other things. However, there is no unique way of aggregating forecasts (or forecast probability density functions) into a measure of disagreement. The paper deals with this problem, surveying some proposed methods. The most appropriate measure of disagreement turns out to depend on the intended use, that is, on the model. Moreover, forecasters underestimate uncertainty. Constitutions and Central-Bank Independence: An Objection to McCallum's Second Fallacy, joint with Giancarlo Spagnolo , is an excursion into the field of Political Economy. The essay provides some foundations for the assumption that renegotiating a delegation contract can be costly by illustrating how political institutions can generate inertia in re-contracting, reduce the gains from it or prevent it altogether. Once the nature of renegotiation costs has been clarified, it is easier to see why certain institutions can mitigate or solve dynamic inconsistencies better than others. / Diss. Stockholm : Handelshögsk., 2001

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23711号 / 理博第4801号 / 新制||理||1688(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 王 功輝, 教授 釜井 俊孝, 教授 久家 慶子 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Hydrologic and climate models predict variables through a simplification of the underlying complex natural processes. Model development involves minimising predictive uncertainty. Predictive uncertainty arises from three broad sources which are measurement error in observed responses, uncertainty of input variables and model structural error. This thesis introduces ways to improve predictive accuracy of hydroclimatic models by considering input and structural uncertainties. The specific methods developed to reduce the uncertainty because of erroneous inputs and model structural errors are outlined below. The uncertainty in hydrological model inputs, if ignored, introduces systematic biases in the parameters estimated. This thesis presents a method, known as simulation extrapolation (SIMEX), to ascertain the extent of parameter bias. SIMEX starts by generating a series of alternate inputs by artificially adding white noise in increasing multiples of the known input error variance. The resulting alternate parameter sets allow formulation of an empirical relationship between their values and the level of noise present. SIMEX is based on the theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. The case study relates to erroneous sea surface temperature anomaly (SSTA) records used as input variables of a linear model to predict the Southern Oscillation Index (SOI). SIMEX achieves a reduction in residual errors from the SOI prediction. Besides, a hydrologic application of SIMEX is demonstrated by a synthetic simulation within a three-parameter conceptual rainfall runoff model. This thesis next advocates reductions of structural uncertainty of any single model by combining multiple alternative model responses. Current approaches for combining hydroclimatic forecasts are generally limited to using combination weights that remain static over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting as an improvement of over static weight combination. The model responses are mixed on a pair wise basis using mixing weights that vary in time reflecting the persistence of individual model skills. The concept is referred here as the pair wise dynamic weight combination. Two approaches for forecasting the dynamic weights are developed. The first of the two approaches uses a mixture of two basis distributions which are three category ordered logistic regression model and a generalised linear autoregressive model. The second approach uses a modified nearest neighbour approach to forecast the future weights. These alternatives are used to first combine a univariate response forecast, the NINO3.4 SSTA index. This is followed by a similar combination, but for the entire global gridded SSTA forecast field. Results from these applications show significant improvements being achieved due to the dynamic model combination approach. The last application demonstrating the dynamic combination logic, uses the dynamically combined multivariate SSTA forecast field as the basis of developing multi-site flow forecasts in the Namoi River catchment in eastern Australia. To further reduce structural uncertainty in the flow forecasts, three forecast models are formulated and the dynamic combination approach applied again. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further improvements.

A obtenção de previsões com maior acuracidade é uma necessidade constantemente requerida, em tempos onde há imensa disponibilidade de dados e recursos computacionais cada dia mais eficientes. Tais critérios possibilitaram o desenvolvimento de muitas técnicas de previsão individual ou de métodos de combinação que são considerados eficientes no intuito de reduzir erros. O desenvolvimento de novas técnicas, por sua vez, promove questionamentos quanto à identificação de quantas ou quais técnicas de previsão individual combinar. A literatura não é unânime ao tentar responder a estes questionamentos e indica a importância da correlação entre os erros de previsão na precisão da combinação. Posto isso, esta tese apresenta uma alternativa aos métodos atuais de combinar previsões, contemplando a correlação entre os erros de previsão, além de propor uma forma de identificar técnicas de previsão que sejam distintas quanto à modelagem de características da série de dados. Para identificar grupos de técnicas de previsão individual que sejam similares, utilizou-se a Análise de Agrupamentos em erros gerados por 15 técnicas de previsão que modelaram uma mesma série de dados real com tendência e sazonalidade. O resultado indicou a formação de 3 agrupamentos. Como alternativa aos métodos atuais de combinar previsão e selecionar a quantidade adequada de técnicas, utilizou-se a Análise de Componentes Principais. O método proposto mostrou-se viável quando comparado com outros métodos de combinação e quando submetido à modelagem de séries com maior variabilidade. / The obtaining of more accurate forecasts is a necessity often required in times where there is a huge availability of data and computing resources becoming more efficient every day. These criteria allowed the development of many individual forecasting techniques or combination methods that are considered efficient in order to reduce errors. The development of new techniques, in turn, promotes questioning as the identification of how many or which techniques to combine individual forecasts. The literature is not unanimous when trying to answer these questions and indicates the importance of the correlation between forecast errors on the accuracy of the combination. That said, this presents an alternative to current methods of combining forecasts, considering the correlation between forecast errors, and propose a way to identify predictive techniques that are different about the modeling features of the data series. To identify groups of individual forecasting techniques that are similar, it was used the cluster analysis on errors generated by 15 forecasting techniques that shaped the same series of real data with trend and seasonality. The result indicated the formation of 3 clusters. As an alternative to current methods of combining forecasting and selecting the appropriate amount of techniques, it was used the Principal Component Analysis. The proposed method has proved feasible when compared to other methods of combining and when subjected to modeling of series with greater variability.

Economic agents often face situations, where there are multiple competing fore- casts available. Despite five decades of research on forecast combinations, most of the methods introduced so far fail to outperform the equal weights forecast combination in empirical applications. In this study, we gather a wide spectrum of forecast combination methods and reexamine these findings in two different classical economic times series forecasting applications. These include out-of- sample combining forecasts from the ECB Survey of Professional Forecasters and forecasts of the realized volatility of the U.S. Treasury futures log-returns. We asses the performance of artificial predictions markets, a class of machine learning methods, which has not yet been applied to the problem of combin- ing economic times series forecasts. Furthermore, we propose a new simple method called Market for Kernels, which is designed specifically for combining time series forecasts. We found that equal weights can be significantly out- performed by several forecast combinations, including Bates-Granger methods and artificial prediction markets in the ECB Survey of Professional Forecasters application and by almost all examined forecast combinations in the financial application. We also found that the Market for Kernels forecast...

[pt] Essa dissertação teve como objetivo propor dois novos métodos para detecção de viés na previsão de demanda. Os métodos consistem numa adaptação de duas técnicas de controle estatístico de processos, o gráfico de controle de EWMA e o algoritmo CUSUM, ao contexto de detecção de viés na previsão de demanda. O desempenho dos métodos foi analisado por simulação, para diversos casos de mudança na inclinação (tendência) da série de dados (mudança de modelo constante para modelo com tendência; alteração na tendência de série crescente; estabilização de série crescente em um patamar constante), e com diferentes parâmetros para os métodos. O estudo limitou-se a séries sem sazonalidade e aos métodos de previsão de amortecimento exponencial simples e de Holt. Os resultados mostraram a grande superioridade do gráfico de EWMA proposto e apontam questões para pesquisas futuras. / [en] The purpose of this dissertation is to propose two new methods for detection of biases in demand forecasting. These methods are adaptations of two statistical process control techniques, the EWMA control chart and the CUSUM control chart (or CUSUM algorithm), to the context of the detection of biases in demand forecasting. The performance of the proposed methods was analyzed by simulation, for several magnitudes of changes in the trend of the series (change from a level series to a series with a trend, changes in the trend parameter, and stabilization of a series with a trend in a constant average level) and with different parameters for all methods. The study was limited to non-seasonal models and to the methods of simple exponential smoothing and Holt’s Exponential Smoothing. The results have shown the superiority of the EWMA method proposed and indicate issues for future research.

Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-08T20:51:33Z No. of bitstreams: 1 LEYDSON GALVÍNCIO DANTAS – DISSERTAÇÃO (PPGMET) 2016.pdf: 2738181 bytes, checksum: db3cee5c7f083124e00eb4eb3220492c (MD5) / Made available in DSpace on 2018-08-08T20:51:33Z (GMT). No. of bitstreams: 1 LEYDSON GALVÍNCIO DANTAS – DISSERTAÇÃO (PPGMET) 2016.pdf: 2738181 bytes, checksum: db3cee5c7f083124e00eb4eb3220492c (MD5) Previous issue date: 2016-02-19 / CNPq / A variabilidade espacial e temporal das precipitações no estado da Paraíba, proporciona irregularidades na distribuição da mesma, como frequentemente observado nos eventos de secas prolongadas e excesso de precipitação em intervalos curtos de tempo, comprometendo em termos gerais a qualidade de vida da população. Neste trabalho é elaborado o preenchimento das falhas encontradas nas séries temporais em alguns municípios do Estado, obtendo deste modo, uma séria robusta o bastante, na qual tem a função de auxiliar, as análises dos resultados ao ser trabalhada com a metodologia de Box-Jenkins para séries temporais. Visando assim, a elaboração de prognósticos pluviométricos em algumas cidades do Estado por intermédio de modelos estocásticos, onde nota-se que a forma do modelo SARIMA(1,0,1)×(1,1,1) é o que melhor se ajusta pela análise dos critérios e resíduos, dentre os municípios onde as previsões chegaram a equivaler alguns dos valores observados nas estações pluviométricas. Os municípios localizados nas proximidades do Planalto da Borborema foram os que demonstraram a melhor acurácia em termos de previsibilidade ao ser comparado com os outros analisados neste estudo. / The spatial and temporal variability of rainfall in the state of Paraiba, provides irregularities in the distribution of the same, as often observed in prolonged droughts events and excess rainfall in short time intervals, affecting generally the quality of life of the population. This paper prepared filling the gaps found in the time series in some cities of the State, thereby obtaining a robust serious enough, which has a helper function, the analysis of the results to be crafted with the Box-Jenkins methodology for time series. Aiming thus the development of rainfall predictions in some cities of the State through stochastic models, where it is noted that the model shape SARIMA(1,0,1)×(1,1,1) is the best fit for analysis the criteria and waste from the municipalities where the predictions come to equate some of the values observed in the rainfall stations. The Cities located near of the Planalto of Borborema were the ones that showed the best accuracy in terms of predictability when compared to the other analyzed in this study.