Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation. The original objective function of three dimensional data assimilation (3D-VAR) consists of two terms: the difference between the pervious forecast and analysis and the difference between the observations and analysis in observation space. Considering the inaccuracy of previous forecasting results, we replace the first term by the difference between the previous forecast gradients and analysis gradients. The associated data fitting term can be interpreted using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3D-VAR setting. In our approach, it is not necessary to estimate the background error covariance matrix and to deal with its inverse in the 3D-VAR algorithm. Indeed, the existence and uniqueness of the analysis solution of the proposed objective function are already established. Instead, the solution can be calculated using the conjugate gradient method iteratively. We present the experimental results based on WRF simulations. We show that the performance of this forecast gradient based DA model is better than that of 3D-VAR. Next, we propose another optimization method of variational data assimilation. Using the tensor completion in the cost function for the analysis, we replace the second term in the 3D-VAR cost function. This model is motivated by a small number of observations compared with the large portion of the grids. Applying the alternating direction method of multipliers to solve this optimization problem, we conduct numerical experiments on real data. The results show that this tensor completion based DA model is competitive in terms of prediction accuracy with 3D-VAR and the forecast gradient based DA model. Then, 3D-VAR and the two model proposed above lack temporal information, we construct a third model in four-dimensional space. To include temporal information, this model is based on the second proposed model, in which introduce the total variation to describe the change of atmospheric state. To this end, we use the alternating direction method of multipliers. One set of experimental results generates a positive performance. In fact, the prediction accuracy of our third model is better than that of 3D-VAR, the forecast gradient based DA model, and the tensor completion based DA model. Nevertheless, although the other sets of experimental results show that this model has a better performance than 3D-VAR and the forecast gradient based DA model, its prediction accuracy is slightly lower than the tensor completion based model.

A previsão de demanda desempenha um papel de fundamental importância dentro das organizações, pois através dela é possível obter uma declaração antecipada do volume demandado no futuro, permitindo aos gestores a tomarem decisões mais consistentes e alocarem os recursos de modo eficaz para atender esta demanda. Entretanto, a eficiência na tomada de decisões e alocação dos recursos requer previsões cada vez mais acuradas. Diante deste contexto, a combinação de previsões tem sido amplamente utilizada com o intuito de melhorar a acurácia e, consequentemente, a precisão das previsões. Este estudo tem por objetivo fazer a adaptação de um modelo de previsão para estimar a demanda de produtos destinados à suplementação animal através da combinação de previsões, considerando as variáveis que possam impactar na demanda e a opinião de especialistas. O trabalho está estruturado em dois artigos, sendo que no primeiro buscou-se priorizar e selecionar, através do Processo Hierárquico Analítico (AHP), variáveis que possam impactar na demanda para que estas pudessem ser avaliadas na modelagem via regressão do artigo 2. Por sua vez, no segundo artigo, realizou-se a adaptação do modelo composto de previsão idealizado por Werner (2004), buscando uma previsão final mais acurada. Os resultados obtidos reforçam que as previsões, quando combinadas, apresentam desempenhos superiores para as medidas de acurácia MAPE, MAE e MSE, em relação às previsões individuais. / The demand prediction has a role of fundamental importance inside the organizations, because trough it is possible to obtain a previous declaration of the demanded amount in the future, allowing the managers to take more consistent decisions and to allocate the resources in an efficient manner in order to satisfy this demand. However, the efficiency in the support decision and resource allocation demands accurated predictions. So, the combination of predictions have been used with the aim of improving the accuracy and, consequently, the precision of the prediction. This study has as objective to do an adaptation of a prediction model to estimate the demand of products designated to animal supplementation through the combination of prediction, considering the variables that can impact in the demand and in the expert opinion. The work is structured in two papers, considering that the first searches to priorize and select through the Analitic Hierarch Process (AHP), variables that can impact in the demand, so they could be evalute in the regression modelling of the paper 2. By the way, in the second paper, it was done an adaptation of the composed prediction model proposed by Werner (2004), searching for a more accurated final prediction. The obtained results reinforce that the prediction, when combined, present superior performance to the accuracy metrics MAPE, MAE and MSE, in relation to the individual predictions.

The objective of this research is to make a theoretical and empirical investigation into the application of a cashflow accounting system. The theoretical investigation provides a definition for cash-flow accounting; it also examines the major arguments for a cash-flow reporting system. Three hypotheses are proposed for testing. The first states that during periods of changing prices, performance indicators that are based on the conventional accrual accounting will diverge from performance indicators that are based on cash-flow accounting and will continue to diverge over time. The second states that this divergence will disappear if the effects of inflation are partialled out. The third states that cash-flow statements, properly interpreted, will enable users to predict business failure.

Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.

Report to Hydrology Laboratory, Office of Hydrology, National Weather Service, NOAH, Dept. of Commerce, Contract 6 -35229 / The value of a forecast system in preventing urban property damage depends on the accuracy of the forecasts, the time at which they are received, the response by the floodplain dweller and the êfficacy of that response. A systems model of the overall flood forecast -response system is developed. Evaluation of the system is accomplished by a decision theoretic methodology. A case study is done for Milton, Pennsylvania, which evaluates the present system and potential changes to it. It is concluded that the sequential nature of the forecast sequence must be considered in modeling the flood forecast -response system if a meaningful evaluation of the economic value of the system is to be obtained. Methodology for obtaining the parameterization of the model from the available data is given. Computer programs have been written to handle a good portion of the calculations. While more work is needed on obtaining accurate parameterization of certain parts of the model, such as the actual response to forecasts; use of the procedures and programs as they now stand produces reasonable evaluations.

A previsão de demanda desempenha um papel de fundamental importância dentro das organizações, pois através dela é possível obter uma declaração antecipada do volume demandado no futuro, permitindo aos gestores a tomarem decisões mais consistentes e alocarem os recursos de modo eficaz para atender esta demanda. Entretanto, a eficiência na tomada de decisões e alocação dos recursos requer previsões cada vez mais acuradas. Diante deste contexto, a combinação de previsões tem sido amplamente utilizada com o intuito de melhorar a acurácia e, consequentemente, a precisão das previsões. Este estudo tem por objetivo fazer a adaptação de um modelo de previsão para estimar a demanda de produtos destinados à suplementação animal através da combinação de previsões, considerando as variáveis que possam impactar na demanda e a opinião de especialistas. O trabalho está estruturado em dois artigos, sendo que no primeiro buscou-se priorizar e selecionar, através do Processo Hierárquico Analítico (AHP), variáveis que possam impactar na demanda para que estas pudessem ser avaliadas na modelagem via regressão do artigo 2. Por sua vez, no segundo artigo, realizou-se a adaptação do modelo composto de previsão idealizado por Werner (2004), buscando uma previsão final mais acurada. Os resultados obtidos reforçam que as previsões, quando combinadas, apresentam desempenhos superiores para as medidas de acurácia MAPE, MAE e MSE, em relação às previsões individuais. / The demand prediction has a role of fundamental importance inside the organizations, because trough it is possible to obtain a previous declaration of the demanded amount in the future, allowing the managers to take more consistent decisions and to allocate the resources in an efficient manner in order to satisfy this demand. However, the efficiency in the support decision and resource allocation demands accurated predictions. So, the combination of predictions have been used with the aim of improving the accuracy and, consequently, the precision of the prediction. This study has as objective to do an adaptation of a prediction model to estimate the demand of products designated to animal supplementation through the combination of prediction, considering the variables that can impact in the demand and in the expert opinion. The work is structured in two papers, considering that the first searches to priorize and select through the Analitic Hierarch Process (AHP), variables that can impact in the demand, so they could be evalute in the regression modelling of the paper 2. By the way, in the second paper, it was done an adaptation of the composed prediction model proposed by Werner (2004), searching for a more accurated final prediction. The obtained results reinforce that the prediction, when combined, present superior performance to the accuracy metrics MAPE, MAE and MSE, in relation to the individual predictions.

It is the purpose of this investigation to determine the amount of rain fall to be expected in various areas of the State of Florida. To date there are no published papers which are concerned with this aspect of rain fall in the state. Although this study is mainly concerned with the amount of rain fall which may be expected at ten per cent intervals throughout the state, it is probable that it has many ramifications which will be of value in other phases of water research. In speaking of the ten per cent intervals of time we are actually dealing with percentages of the total time. Hence, 25 years out of 50 years should not be construed to mean the next 25 years out of the next 50 years. / Typescript. / "August, 1949." / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science under Plan II." / Advisor: LeRoy Babcock, Professor Directing Paper. / Includes bibliographical references (leaf 58).

The thesis concerns the topic of forecasting using Neural Networks, particu- larly the return and volatility forecasting in the volatile period of Covid-19. The thesis uses adjusted close daily data from Jan 1, 2000, until Jan 1, 2021, of the S&P index and Prague Exchange Stock index (PX). The comparison was between the vanilla econometrical model, a neural network model, and a hybrid neural network model. Hybrid neural networks were constructed with an additional feature column of the fitted econometrical model. Additionally to comparing the prediction, a risk-return trade-o analysis of the forecasted series was conducted. The test period for all models was from Jan 1, 2020, until Jan 1, 2021, where predictions were made. During the test period, MSE be- tween predicted and true values was extracted and compared. The results are that the hybrid model outperformed both econometrical as well as only neural networks models. Furthermore, the risk-return trade-o forecast provided by the hybrid model fares better than the other ones. JEL Classification C53, C81 Keywords Financial Time Series, Forecasting, Neural Net- works, ARIMA, GARCH Title Forecasting with Neural Network during Covid- 19 Crisis Author's e-mail tiep.luud@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz

This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).

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