Este trabalho tem como objetivo a avaliação de três estimadores do parâmetro de integração fracionária d e de um teste para memória longa compartilhada. Os estimadores a serem avaliados são: o estimador de Geweke e Porter-Hudak, o estimador usando o periodograma suavizado e o estimador semiparamétrico truncado de Whittle. A avaliação dos estimadores será no contexto de processos ARFIMA+ARMA, e em relação a variações nos termos autoregressivos e de médias móveis, tanto do termo de memória curta quanto do termo de memória longa. Além disso, serão introduzidos o conceito de modelos com memória longa compartilhada e um método de identificação através da análise de correlação canônica para séries temporais multivariadas proposto, por Ray e Tsay (1997). Por fim, serão apresentadas três aplicações sobre dados reais dos tópicos estudados: uma para a velocidade do vento em São Paulo e Piracicaba e outras duas para séries das bolsas de valores de Hong Kong, Nova Zelândia, Singapura, Brasil e Reino Unido / The goal of this project is the evaluation of three long memory parameter estimators and a common long range dependence test. The estimators evaluated are: the Geweke and Porter-Hudak, the smoothed periodogram and the semiparametric truncated Whittle estimators. The evaluation is in the context of processes ARFIMA+ARMA, and related to variations in the autoregressive and moving average coefficients, both in the short and long memory terms. Furthermore, we describe common long range dependence processes and an identification approach (Ray and Tsay, 1997) for them, using the canonical correlation analysis. Finally, three applications to real data are presented: the first one to the wind\'s speed in the Brazilian cities of São Paulo and Piracicaba, and the other ones to financial time series of the stock markets of Hong Kong, New Zealand, Singapore, Brazil and the United Kingdom.

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Made available in DSpace on 2015-03-26T13:32:17Z (GMT). No. of bitstreams: 1 texto completo.pdf: 3004404 bytes, checksum: 18834db766750ae443a52c29a9b0decd (MD5) Previous issue date: 2012-07-24 / Fundação de Amparo a Pesquisa do Estado de Minas Gerais / Time series are sometimes influenced by disruptions of events, such as strikes, the outbreak of war, among others. These interrupts originate atypical observations or outliers that directly influence the homogeneity of the series, leading to erroneous inferences and interpretations of the variable under study, being very common in climatological data. So, in the interest of detecting outliers in time series of precipitation, this study aimed to establish a method of detecting outliers. For this, there was the junction of ARIMA models and methodologies of the classical geostatistics, the self-validation. The proposed criterion compares waste of time series analysis with confidence intervals of the residue of self-validation. We analyzed time series of average monthly rainfall for rainy days of 43 rainfall stations in the state of Minas Gerais, between the years 2000 to 2005. The analysis procedures ranging from the description of the periodicity through the periodogram to obtain validation, from the estimation of the semivariogram models by ordinary least squares methods and maximum likelihood. The results for the period under study, 165 were detected outliers, spread between the 43 rainfall stations. The station Campo Grande Ranch, located in the municipality of Passa Tempo, was the season in which they recorded the highest number of outliers, 45 in total. As the results, we considered the proposed method very efficient in detecting outliers, and therefore the analysis of the homogeneity of observations. / Séries temporais são algumas vezes influenciadas por interrupções de eventos, tais como greves, eclosão de guerras, entre outras. Estas interrupções originam observações atípicas ou outliers que influenciam diretamente na homogeneidade da série, ocasionando interpretações e inferências errôneas da variável sob estudo, sendo muito comum em dados climatológicos. Assim, com o interesse de detectar outliers em séries temporais de precipitação, o presente trabalho teve por objetivo estabelecer um método de detecção outliers. Para tal, realizou-se a junção da modelagem ARIMA e de uma das metodologias clássicas de geoestatística, a autovalidação. O critério proposto compara os resíduos da análise de séries temporais com intervalos de confiança dos resíduos da autovalidação. Foram analisadas séries temporais da precipitação média mensal por dias chuvosos de 43 estações pluviométricas localizadas no estado de Minas Gerais, entre os anos de 2000 a 2005. Os procedimentos de análise vão da descrição da periodicidade por meio do periodograma até a obtenção da autovalidação, à partir da estimação dos modelos de semivariograma pelos métodos de mínimos quadrados ordinários e máxima verossimilhança. Pelos resultados, para o período sob estudo, foram detectado 165 outliers, espalhados entre as 43 estações pluviométricas. A estação Fazenda Campo Grande, localizada no município de Passa Tempo, foi a estação em que se registrou o maior número de outliers, 45 no total. Conforme os resultados obtidos considerou-se o método proposto muito eficiente na detecção de outliers e, consequentemente, na análise da homogeneidade das observações.

Quality tools, more specifically control charts, are important statistical resources to know and to monitor production processes. Their goal is to find the common and notable causes of a process to, through monitoring, increase the stability and, from it, assess if the process is under control. The dynamics of today s industrial activities has raised new requirements for good monitoring, and in that sense, new control tools have been developed and these are able to understand the new causal relationships among variables. The research shows the use of three modeling methodologies to treat autocorrelated data enabling to monitor a productive electroplating process. Initially, it was carried out a descriptive analysis for the verification of normality and independence and, afterwards, ARIMA from Box and Jenkins models, ARMAX models of multiple linear regression, MRLM, for the subsequent construction of waste control charts. In addition to the provided academic knowledge, it presents more than one application of control charts to the industrial environment, and also collaborates with the company where the research was developed showing which of the methods is more effective in controlling the production. The best result obtained by monitoring these three statistical methodologies work when confronted with the conventional control method, i.e., without treating the autocorrelation, it was used ARIMA model and a subsequent application of waste control charts derived from this modeling. The decision of the most effective methodology for modeling electroplating was defined by the number of points found out of the conventional limits established. The one that better captured the fluctuations of the process was obtained with the residues of ARIMA. / As ferramentas da qualidade, mais especificamente as cartas de controle, são importantes recursos estatísticos para se conhecer e monitorar processos produtivos, sendo que seu objetivo é encontrar as causas comuns e assinaláveis de um processo para, com seu monitoramento, aumentar sua estabilidade e, a partir daí, considerar se o processo está sob controle. A dinâmica das atividades industriais hoje existentes fez surgir novas necessidades para um bom monitoramento, e, nesse sentido, novas ferramentas de controle foram desenvolvidas, capazes de entender as novas relações causais entre as variáveis. A pesquisa apresenta o uso de três metodologias de modelagem para tratar dados autocorrelacionados possibilitando o monitoramento de um processo produtivo de eletrogalvanização. Inicialmente foi realizada uma análise descritiva para a verificação de pressupostos de normalidade e independência e após foram ajustados os modelos ARIMA de Box e Jenkis, modelos ARMAX e modelos de regressão linear múltipla, MRLM, para posterior construção das cartas de controle dos resíduos. Além do conhecimento acadêmico proporcionado, apresenta mais de uma aplicação das cartas de controle ao ambiente industrial, e também colabora com a empresa onde a pesquisa foi desenvolvida mostrando qual das metodologias é mais efetiva no controle da produção. O melhor resultado de monitoramento obtido com o trabalho estatístico nessas três metodologias quando confrontado com o método de controle convencional, ou seja, sem tratar a autocorrelação foi utilizando a modelo ARIMA e posterior aplicação dos gráficos de controle de resíduos oriundos desta modelagem. A decisão da metodologia de modelagem mais eficaz para a eletrogalvanização foi definida pelo número de pontos encontrados fora dos limites convencionais estabelecidos. A que melhor captou as flutuações do processo foi a obtida com os resíduos do ARIMA.

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.

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.

Embedded platforms are often used as a sensor data processing node to collect data and transmit the data to the remote server. Due to the poor performance and power limitation, data processing was often left to the remote server. With the improvement of the computation ability, it is becoming possible to do some partial data processing on the embedded platforms, which would reduce the power and time consumption on the data transmission. Moreover, processing the data locally on the embedded platforms could reduce the dependence on the network. The platform could even do some tasks offline. This project aims to explore effective data analysis methods, especially for anomaly detection, which could be implemented on the embedded platform to be analyzed and detected locally. In this project, we select four methods: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long ShortTerm Memory (LSTM), to implement on the embedded platform ESP32. To test which methods could better fit the platform, we evaluate and compare the result from two aspects: the time overhead and the accuracy. The results show that the STL has the highest detection accuracy, but its time overhead is significantly higher than all other methods. ARIMA has the smallest time overhead and higher accuracy than LSTM and VAR. For LSTM, the method performs better with univariable input than multivariable input. Finally, we discuss the factors that may influence the result and future works. / Inbäddade plattformar används ofta som en sensor databehandlingsnod för att samla in och sedan överföra data till fjärrservern. Databehandling lämnades ofta till fjärrservern på grund av den dåliga prestandan och effektbegränsningen. Med förbättrad beräkningsförmåga blir det framkomligt att göra en del databehandling på de inbäddade plattformarna, vilket skulle minska ström och tidsförbrukningen för dataöverföringen. För övrigt kan lokal behandling av data på de inbäddade plattformarna minska beroendet av nätverket. Plattformen kan till och med utföra vissa uppgifter I nedkopplat läge. Detta projekt avser att utforska effektiva dataanalysmetoder särskilt för avvikelsedetektering, som kan verkställas på den inbäddade plattformen för att analyseras och upptäckas lokalt. I det här projektet väljer vi fyra metoder för att införa på den inbäddade plattformen ESP32: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long Short-Term Memory (LSTM). För att testa vilka metoder som bättre passar plattformen utvärderar och jämför vi resultatet med hänsyn till två aspekter: tidsomkostnaderna och noggrannheten. Resultaten visar att STL har den högsta detektionsnoggrannheten, men dess tidsomkostning är betydligt högre än alla andra metoder. ARIMA har den minsta tidsomkostningen och högre noggrannhet än LSTM och VAR. För LSTM fungerar metoden bättre med univariable input än multivariable input. Slutligen diskuterar vi faktorerna som möjligtvis påverkar resultatet och framtida arbeten.

Forecasting financial time series is one of the most challenging problems in economics and business. Markets are highly complex due to non-linear factors in data and uncertainty. It moves up and down without any pattern. Based on historical univariate close prices from the S\&P 500, SSE, and FTSE 100 indexes, this thesis forecasts future values using two different approaches: one using a classical method, a Seasonal ARIMA model, and a hybrid ARIMA-GARCH model, while the other uses an LSTM neural network. Each method is used to perform at different forecast horizons. Experimental results have proven that the LSTM and Hybrid ARIMA-GARCH model performs better than the SARIMA model. To measure the model performance we used the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.

During several decades the stock market has been an area of interest forresearchers due to its complexity, noise, uncertainty and nonlinearity of thedata. Most of the studies regarding this area use a classical stochastics method,an example of this is ARIMA which is a standard approach for time seriesprediction. There is however another method for prediction of the stock marketthat is gaining traction in the recent years; Artificial Neural Network (ANN).This method has mostly been used in research on the American and Asian stockmarkets so far. Therefore, the purpose of this essay was to explore if ArtificialNeural Network could be used instead of ARIMA to predict the Swedish stockmarket (OMXS30). The study used data from the Swedish Stock Marketbetween 1991-07-09 to 2018-12-28 for the training of the ARIMA model anda forecast data that ranged between 2019-01-02 to 2019-04-26. The forecastdata of the ANN was composed of 80% of the data between 1991-07-09 to2019-04-26 and the evaluation data was composed of the remaining 20%. TheANN architecture had one input layer with chunks of 20 consecutive days asinput, followed by three Long Short-Term Memory (LSTM) hidden layers with128 neurons in each layer, followed by another hidden layer with RectifiedLinear Unit (ReLU) containing 32 neurons, followed by the output layercontaining 2 neurons with softmax activation. The results showed that theANN, with an accuracy of 0,9892, could be a successful method to forecast theSwedish stock market instead of ARIMA.