This thesis presents a comparison for modeling and forecasting Chinese futures market of copper and aluminum with single time series and multivariate time series under linear restrictions. For single time series, data transformation for stationary purpose has been tested and performed before ARIMA model was built. For multivariate time series, co-integration rank test has been performed and included before VECM model was built. Based on selected models, the forecasting shows multivariate time series analysis has a better result than single time series, which indicates utilizing the relationships among the series can improve the accuracy of time series forecasting.

This paper analyzes effect of gun buyback that took place in Great Britain in years 1996 and 1997 on crime rate and compares the results with theoretical arguments and previous empirical findings. It contains analysis of three independent time series: crime rate in England and Wales, Scotland and Northern Ireland. Models of the time series are built using Box-Jenkins methodology. The models are tested for presence of a structural break using visual analysis, Chow test and Quandt-Andrews test. These tests are used as an evaluation criterion of the effect of buyback on crime rate. The result of the analysis is that it is not possible to reject the null hypothesis that buybacks do not have effect on crime rate.

Por meio do monitoramento da evolução da temperatura da mistura cimento-madeira, pode-se utilizar esta informação como uma série temporal. O objetivo deste estudo foi utilizar modelos de séries temporais para descrever as séries de temperatura do experimento constituído por diferentes espécies associadas a resíduos de Candeia na produção de painéis particulado e compara-las duas a duas para averiguar se foram geradas pelo mesmo processo estocástico. Inicialmente foi realizado um estudo para avaliar a estacionariedade das séries utilizando o correlograma e o teste da raiz unitária de Dickey-Fuller, na qual todas as séries apresentaram não estacionariedade, para o tratamento de 25% Candeia e Eucalipto com tratamento prévio de água foi dita uma série I(2) e pelos critérios AIC, BIC e MAPE o melhor modelo foi ARIMA(2, 2, 2), para o tratamento de 50% Candeia e Eucalipto também com tratamento prévio de água foi dita uma série I(1) e pelos critérios o melhor modelo foi ARIMA(4, 2, 2), para o tratamento de 75% Candeia e Eucalipto com tratamento prévio de água foi dita uma série I(1) com o modelo ARIMA(5, 1, 0), e para o tratamento de 25% Candeia e Eucalipto sem tratamento prévio de água foi dita uma série I(1) com o modelo ARIMA(2, 1, 2). Em relação à comparação das séries temporais contempladas neste trabalho é possível concluir que as mesmas são diferentes entre si, ou seja, não foram geradas pelo mesmo processo estocástico. / By monitoring the temperature evolution of the cement-wood mixture, one can utilize this information as a time series. The objective of this study was to utilize time series models to describe the temperature series from an experiment, consisting of different species associated to Candeia residuals in the production of particleboard panels, and do a pairwise comparison to verify if they were generated from the same stochastic process. Initially it was realized the Dickey-Fuller unit root test to verify series stationarity, which indicated that all series were not stationary. For the 25% Candeia and Eucalyptus treatment with previous water treatment the series was best modelled by an ARIMA(2, 2, 2) as evidenced by the AIC, BIC and MAPE criteria. For the 50% Candeia and Eucalyptus treatment also with previous water treatment the series was best modelled by an ARIMA(4, 2, 2) as indicated by the same criteria. Finally for the 75% Candeia and Eucalyptus treatment with previous water treatment and the 25% Candeia and Eucalyptus treatment without previous water treatment the best models were the ARIMA(5, 1, 0) and the ARIMA(2, 1, 2) respectively. In relation to the comparison of the time series contemplated in this study it is possible to conclude that they are different, that is, they were not generated by the same stochastic process.

nÃo hà / Esta DissertaÃÃo trata de pesquisa sobre a anÃlise de sÃries temporais para previsÃo mensal do Imposto Sobre CirculaÃÃo e Mercadorias e PrestaÃÃo de ServiÃos â ICMS no estado do PiauÃ. 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 FunÃÃo de TransferÃncia para realizar previsÃes, bem como o Modelo CombinaÃÃo de PrevisÃes. A dissertaÃÃo apresenta um diagnÃstico do ICMS no estado do Piauà e uma revisÃo da literatura onde sÃo abordados os principais aspectos teÃricos dos modelos utilizados no trabalho, bem como a anÃlise dos resultados empÃricos. Ao final, pode-se observar que os resultados obtidos na presente dissertaÃÃo, estÃo em sintonia com outros resultados obtidos em trabalhos semelhantes realizados sobre o tema, o que vem a confirmar a importÃncia dos modelos que utilizam a anÃlise de sÃries temporais como instrumento de prediÃÃo. / This dissertation deals with a research on the temporal series analysis for the monthly forecast of the turnover and services tax â ICMS in Brazil â in the state of PiauÃ. 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 PiauÃ, 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.

<p>With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.</p>

With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.

This study investigates the trends and varieties of concentration of ozone in recent years in Taiwan in order to understand the situation of air qualities in different areas. Ozone is the secondary pollutant produced by nitrogen oxides, reactive hydrocarbons and sunlight. Because ozone has strong oxidizing power, it is easy to stimulate the respiratory system, which may cause cough, asthma, headache, tiredness and harmful to lung; and it is also harmful to plants and even synthetic materials. Here, we tried to study the trends and varieties of the time effect to the ozone level in each region and compare the similarities and heterogeneity of the models in different regions by the ozone data obtained from all air quality monitoring stations of environmental protection administration. Analysis of building appropriate temporal and spatial models are performed and factor analysis on the model residuals are used to investigate the possible latent variables to interpret the patterns of the ozone values in different regions. These may help to set up strategies for ozone control in the future.

Por meio do monitoramento da evolução da temperatura da mistura cimento-madeira, pode-se utilizar esta informação como uma série temporal. O objetivo deste estudo foi utilizar modelos de séries temporais para descrever as séries de temperatura do experimento constituído por diferentes espécies associadas a resíduos de Candeia na produção de painéis particulado e compara-las duas a duas para averiguar se foram geradas pelo mesmo processo estocástico. Inicialmente foi realizado um estudo para avaliar a estacionariedade das séries utilizando o correlograma e o teste da raiz unitária de Dickey-Fuller, na qual todas as séries apresentaram não estacionariedade, para o tratamento de 25% Candeia e Eucalipto com tratamento prévio de água foi dita uma série I(2) e pelos critérios AIC, BIC e MAPE o melhor modelo foi ARIMA(2, 2, 2), para o tratamento de 50% Candeia e Eucalipto também com tratamento prévio de água foi dita uma série I(1) e pelos critérios o melhor modelo foi ARIMA(4, 2, 2), para o tratamento de 75% Candeia e Eucalipto com tratamento prévio de água foi dita uma série I(1) com o modelo ARIMA(5, 1, 0), e para o tratamento de 25% Candeia e Eucalipto sem tratamento prévio de água foi dita uma série I(1) com o modelo ARIMA(2, 1, 2). Em relação à comparação das séries temporais contempladas neste trabalho é possível concluir que as mesmas são diferentes entre si, ou seja, não foram geradas pelo mesmo processo estocástico. / By monitoring the temperature evolution of the cement-wood mixture, one can utilize this information as a time series. The objective of this study was to utilize time series models to describe the temperature series from an experiment, consisting of different species associated to Candeia residuals in the production of particleboard panels, and do a pairwise comparison to verify if they were generated from the same stochastic process. Initially it was realized the Dickey-Fuller unit root test to verify series stationarity, which indicated that all series were not stationary. For the 25% Candeia and Eucalyptus treatment with previous water treatment the series was best modelled by an ARIMA(2, 2, 2) as evidenced by the AIC, BIC and MAPE criteria. For the 50% Candeia and Eucalyptus treatment also with previous water treatment the series was best modelled by an ARIMA(4, 2, 2) as indicated by the same criteria. Finally for the 75% Candeia and Eucalyptus treatment with previous water treatment and the 25% Candeia and Eucalyptus treatment without previous water treatment the best models were the ARIMA(5, 1, 0) and the ARIMA(2, 1, 2) respectively. In relation to the comparison of the time series contemplated in this study it is possible to conclude that they are different, that is, they were not generated by the same stochastic process.

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.

The aim of the thesis mainly is to understand an issue of time series analysis. There are many methods in time series analysis, but purpose of this analysis persists the same, which is a construction of sufficient model of time series and his application in forecasting of time series. We have to make a basic identification of time series to establish right process in model constructing. The first and the second chapter is devoted to this basic identification. There are many methods, how we said before, for constructing of concrete model. In this thesis, exactly in the third chapter, we introduce one of the most flexible methodology of model constructing. That is The Box-Jenkins methodology, which was defined in 1976 by these men. In the last chapter we try to put to use insight in the issue of time series analysis for comparison and separation of the space of time series and this comparison use for the right interpretation of the parameters of time series model. The diploma project was supported by project from MSMT of the Czech Republic no. 1M06047 "Centre for Quality and Reliability of Production".