During the last years around 100 000 immigrants have arrived to Sweden, people with different reasons and different goals for settling down in Sweden. The reason for immigrating to Sweden that will be dealt with in this thesis is following immigration, i.e. when someone moves here because they have relatives living in the country. The reason why it is interesting to study following immigration is that it is an affecting factor for how many that will immigrate to Sweden the following years and may then be used to make a forecast, based on how many first time immigrants there are. To be able to investigate the following immigration analyses are made with time series, logistic regression and Poisson regression. An ARIMA-model has been used to estimate the number of following immigrants in the future. The other part of this thesis will inquire the matter how inclined immigrants are to become Swedish citizens, whether they even apply for citizenship and also how long time it takes from the time when they fulfil the conditions for Swedish citizenship until they apply. Here also multiple logistic regression will be used and then ordinary regression. The most common reason for permitted residence in Sweden is following immigration. Following immigration has increased since 1998, mainly over the last years there has been a substantial immigration increase. It is difficult to predict how the immigration will develop during the following years due to the occurred growth of immigrants at the end of the study period. Since 1998 about 5% of the persons that have got permitted residence in Sweden are association persons. Most common to be an association person is an older man and the reason he got permitted residence was asylum. The association persons have in average 3,16 following immigrants tied to them. To be Swedish citizen through naturalization there are conditions that need to be fulfilled, for example, the immigrant has to have been settled in Sweden for a certain time. For the immigrants that fulfil this time condition there are about 79 % that apply for Swedish citizenship. The largest probability that an immigrant apply for citizenship occur if the person is young, woman and following immigrant. The ones that apply for citizenship are waiting in average 57 days until they are applying after they fulfil the time condition. / Under 2008 invandrade drygt 100 000 personer till Sverige, personer som invandrade av olika skäl och med olika mål med sin bosättning i Sverige. Den anledning för invandring till Sverige som framförallt behandlas i den här uppsatsen är att man har anhöriga i landet, vilket kallas följdinvandring. Anledningen till att det är intressant att studera följdinvandring är att det är en påverkande faktor för hur många som kommer att invandra till Sverige under kommande år och kan alltså användas för prognoser, utifrån hur stort antalet förstagångsinvandrare är. För att kunna undersöka följdinvandringen analyseras den med tidsserier, logistisk regression och Poissonregression. Till skattningar av antalet följdinvandrare i framtiden har en ARIMA-modell anpassats. Den andra delen av uppsatsen kommer att undersöka hur benägna invandrare är att bli svenska medborgare. Av intresse är om de alls ansöker om medborgarskap och givet att de gör det hur lång tid det tar ifrån det att de uppfyller villkoren för svenskt medborgarskap till dess att de ansöker. Även här kommer logistisk regression att användas och sedan linjär regression. En av de vanligaste anledningarna till att få uppehållstillstånd är att man är följdinvandrare. Följdinvandringen har ökat sedan 1998, framför allt under de senaste åren då en kraftig ökning kan skönjas. Att en så stark ökning inträffar i slutet av perioden gör det svårt att förutsäga hur följdinvandringen kommer utvecklas inom de närmaste åren. Av de personer som sedan 1998 fått uppehållstillstånd i Sverige är idag ungefär 5 % anknytningspersoner. Att bli anknytningsperson är vanligast om man är äldre, man och har fått uppehållstillstånd på grund av asyl. Anknytningspersonerna hade i genomsnitt 3,16 följdinvandrare knutna till sig. För att kunna bli svensk medborgare genom naturalisation krävs bland annat att man haft sin hemvist i Sverige under en viss tid. Av dem som uppfyllt tidskravet ansöker ungefär 79 % om medborgarskap. Störst sannolikhet att en person ska ansöka om medborgarskap är det om personen är ung, kvinna och följdinvandrare. De som ansöker om medborgarskap väntar i genomsnitt 57 dagar tills de ansöker efter det att de uppfyllt tidskravet.

A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two different approaches are used for forecasting the daily call volume Box and Jenkins (ARIMA) methodology and Smoothing methodology. We generate the models for forecasting of call volume and present a comparison of the two different techniques.

Using both single and vector processes, we fitted the Box-Jenkin’s ARIMA model and the Vector Autoregressive model following the Johansen approach, to forecast soy bean and green bean prices on the Chinese futures markets. The results are encouraging and provide empirical evidence that the vector processes perform better than the single series. The co-integration test indicated that the null hypothesis of no co-integration among the relevant variables could be rejected. This is one of the most important findings in this paper. The purposes for analyzing and modeling the series jointly are to understand the dynamic relationships over time among the series and improve the accuracy of forecasts for individuals series by utilizing the additional information available from the related series in the forecasts for each series.

The rapid growth of online games enables firms to charge players for virtual goods they sell for use within their online game environments. Determining prices for such virtual goods is inherently challenging due to the absence of explicit supply curve as the marginal cost of producing additional virtual goods is negligible. Utilizing sales data, we study daily revenue of a firm operating a virtual world and selling cards. Explicitly, we analyze the impact of new product releases on revenue using ARIMA with intervention model. We show that during initial days after a new product release, the firm's daily revenue significantly increases. Using a quality measure, based on the Elo rating method, we can determine the relative good prices based on good usage. Applying this method we show that the rating of a product can be a good proxy for units sold. We conclude that our quality-based measure can be adopted for pricing other virtual goods.

CRUZ, Cristovam Colombo dos Santos. Análise de séries temporais para previsão mensal do ICMS: o caso do Piauí. 2007. 81f. Dissertação (mestrado profissional) - Programa de Pós-Graduação em Economia, CAEN, Universidade Federal do Ceará, Fortaleza-CE, 2007. / Submitted by Mônica Correia Aquino (monicacorreiaaquino@gmail.com) on 2013-11-13T22:36:04Z No. of bitstreams: 1 2007_dissert_ccscruz.pdf: 416574 bytes, checksum: f7d6cde0d07aeff034c1c62ed652af3f (MD5) / Approved for entry into archive by Mônica Correia Aquino(monicacorreiaaquino@gmail.com) on 2013-11-13T22:36:16Z (GMT) No. of bitstreams: 1 2007_dissert_ccscruz.pdf: 416574 bytes, checksum: f7d6cde0d07aeff034c1c62ed652af3f (MD5) / Made available in DSpace on 2013-11-13T22:36:16Z (GMT). No. of bitstreams: 1 2007_dissert_ccscruz.pdf: 416574 bytes, checksum: f7d6cde0d07aeff034c1c62ed652af3f (MD5) Previous issue date: 2007 / This dissertation deals with a research on the temporal series analysis for the monthly forecast of the turnover and services tax – ICMS in Brazil – in the state of Piauí. The aim of this research is to offer the statewide policymakers a consistent forecast and powerfully predictive model, so as to contribute to the state finance management. In this work, the ARIMA and Assignment Function models were used to carry out forecasts, as well as Forecast Combination. The dissertation presents a diagnosis of the ICMS in the state of Piauí, a review on the literature where the main theoretical aspects of the models carried out in the work are addressed, in addition to the empirical findings analysis. As a conclusion, it can be observed that the findings carried out in this dissertation are in harmony with other results of similar works carried out on the theme, which corroborates the importance of the models using the temporal series analysis as a forecasting instrument. / Esta Dissertação trata de pesquisa sobre a análise de séries temporais para previsão mensal do Imposto Sobre Circulação e Mercadorias e Prestação de Serviços – ICMS no estado do Piauí. Objetiva-se com essa pesquisa oferecer aos gestores do estado um modelo de previsão consistente e com bom poder preditivo, de forma a contribuir com a gestão financeira estadual. No trabalho, utilizaram-se os modelos ARIMA e Função de Transferência para realizar previsões, bem como o Modelo Combinação de Previsões. A dissertação apresenta um diagnóstico do ICMS no estado do Piauí e uma revisão da literatura onde são abordados os principais aspectos teóricos dos modelos utilizados no trabalho, bem como a análise dos resultados empíricos. Ao final, pode-se observar que os resultados obtidos na presente dissertação, estão em sintonia com outros resultados obtidos em trabalhos semelhantes realizados sobre o tema, o que vem a confirmar a importância dos modelos que utilizam a análise de séries temporais como instrumento de predição.

ALMEIDA, A. F. M. Análise comparativa da aplicação de modelos para imputação do volume médio diário de séries históricas de volume de tráfego. 2010. 100 f. Dissertação (Mestrado em Engenharia de Transportes) - Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2010. / Submitted by Marlene Sousa (mmarlene@ufc.br) on 2012-02-23T15:02:03Z No. of bitstreams: 1 2010_dis_afmalmeida.pdf: 1017311 bytes, checksum: b237d72410de031a5badf23203368e8b (MD5) / Approved for entry into archive by Marlene Sousa(mmarlene@ufc.br) on 2012-02-23T16:05:53Z (GMT) No. of bitstreams: 1 2010_dis_afmalmeida.pdf: 1017311 bytes, checksum: b237d72410de031a5badf23203368e8b (MD5) / Made available in DSpace on 2012-02-23T16:05:53Z (GMT). No. of bitstreams: 1 2010_dis_afmalmeida.pdf: 1017311 bytes, checksum: b237d72410de031a5badf23203368e8b (MD5) Previous issue date: 2010-09 / In order to improve the road system, with regard to its infrastructure and operation, it is necessary to perform studies and planning, by seeking the best use of existing resources. Therefore an important traffic measure is used, i.e., vehicle volume. Traffic data is collected either manually or electronically; however both ways can fail and not collect all data. In the case of electronic counting equipment, the continuous data collection may form a time series, which produces failures in the database due to non-collection, which can compromise the studies based on this information. Therefore this work aims to perform analysis of methods used to estimate these missing values, by trying to know the most effective model for the Average Daily Volume variable of the data obtained by the continuous counting stations installed in the state highways of Ceará. The estimation models used in this work are the ARIMA models for time series analysis, and simple models, which present a less complex application and a faster processing, while the ARIMA requires more specific knowledge of the professional who uses it. The most effective method considered herein was the one that obtained smaller errors after the application of the models. Four permanent counting stations were selected for these applications, according to the percentage of valid data and its location, by seeking the use of stations in representative points of the state. The best model found was ARIMA (1,0,1)7 (with an average error of 1.816%), however one of the simplest models, MS2, produced results similar to those of ARIMA (an average error of 1.837%), and it can also be considered suitable for application in the allocation of missing values. / Para melhorias do sistema rodoviário, tanto no que se refere à infra-estrutura quanto à operação, é necessário a realização de estudos e planejamento, buscando a melhor utilização dos recursos existentes. Para tanto, faz-se o uso de uma importante medida de tráfego, o volume veicular. Os dados de tráfego são coletados por meio manuais ou eletrônicos, porém, ambos podem apresentar falhas e não coletar os dados em sua totalidade. No caso dos equipamentos eletrônicos de contagem, a coleta contínua pode formar uma série histórica, que, devido a não coleta, gera falhas ao longo da base de dados, as quais podem comprometer os estudos embasados nestas informações. Este trabalho busca, portanto, realizar análises de métodos empregados para estimação destes valores faltosos, buscando conhecer o modelo mais eficaz para a variável Volume Médio Diário dos dados obtidos pelos postos de contagem contínua instalados nas rodovias estaduais do Ceará. Os modelos de estimação aplicados neste trabalho são os modelos ARIMA de análise de séries temporais, e modelos simples, que apresentam aplicação menos complexa e processamento mais rápido, enquanto que o ARIMA demanda maior conhecimento específico do profissional que o utiliza. Assim, o método mais eficaz aqui considerado foi o que obteve menores erros após aplicação do modelo. Para estas aplicações foram selecionados quatro postos permanentes, de acordo com o percentual de dados válidos e sua localização, buscando a utilização de postos em pontos representativos do estado. O melhor modelo encontrado foi o ARIMA (1,0,1)7 (com erro médio de 1,816%), porém, um dos modelos simples, o MS2, obteve resultados próximos aos do ARIMA (erro médio 1,837%), e também pode ser considerado satisfatório para aplicação na imputação de valores faltosos.

Made available in DSpace on 2014-06-12T17:17:16Z (GMT). No. of bitstreams: 2 arquivo5969_1.pdf: 1504936 bytes, checksum: c5647bff1c1bd1fe1a5f7cbe7319989a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / A presente tese pretende desenvolver estudos aplicados ao setor elétrico brasileiro - SEB. Inicialmente, é feita uma revisão geral do SEB, enfocando as instituições e os agentes atuantes no setor. Em um primeiro estudo são feitas previsões para a demanda por energia elétrica no Nordeste no período 2004-2010. As previsões foram revisadas, ainda, de forma a levarem em conta o efeito do racionamento de energia elétrica ocorrido em 2001. Como resultado, os modelos ajustados apresentaram um bom poder de explicação e os valores das elasticidades foram próximos aos obtidos em outros estudos similares. Em um outro estudo, utilizou-se modelos ARIMA para previsão de preços spot de energia elétrica no Brasil. Finalmente, para analisar o potencial de exercício de poder de mercado no segmento de geração do SEB, foi utilizado o modelo de oligopólio de Cournot para analisar o impacto de variações na estrutura do setor sobre os preços de equilíbrio do mercado. Nestes termos, observou-se um maior potencial de exercício de poder de mercado em meses de menor disponibilidade de geração hidráulica. Outros fatores importantes no modelo foram a elasticidade da demanda e a estrutura do setor, em termos do número e do tamanho das firmas

Forecasting is a powerful tool that can enable companies to save millions in revenue every year if the forecast is good enough. The problem lies in the good enough part. Many companies today use Excel topredict their future sales and trends. While this is a start it is far from optimal. Seco Analytics aim to solve this issue by forecasting in an informative and easy manner. The web application uses the ARIMA analysis method to accurately calculate the trend given any country and product area selection. It also features external data that allow the user to compare internal data with relevant external data such as GDP and calculate the correlation given the countries and product areas selected. This thesis describes the developing process of the application Seco Analytics.

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

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