Dissertations / Theses on the topic 'ARIMAX model'
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Логін, Вадим Вікторович. "Моделі для прогнозування характеристик трафіка цифрової реклами." Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23748.
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Магістерська дисертація: 112 с., 48 рис., 40 табл., 3 додатки і 30 джерел.
Об’єкт дослідження – трафік цифрової реклами у формі статистичних даних.
Предмет дослідження – моделі та методи аналізу даних у формі часових рядів, методи прикладної статистики.
Мета роботи – побудова моделей часових рядів для прогнозування найважливіших характеристик трафіка цифрової реклами.
Методи дослідження – моделі часових рядів для прогнозування даних та порівняльний аналіз отриманих моделей.
У даній роботі наведені результати побудови моделей часових рядів, що призначені для прогнозування найважливіших характеристик трафіка цифрової реклами. Описані результати порівняльного аналізу отриманих моделей за допомогою інформаційних критеріїв, а також з точки зору їхньої точності. Встановлено, що для нашої задачі, найкращою моделлю є модель ARIMAX (Autoregressive integrated moving-average model with exogenous inputs), тобто модель авторегресії та ковзного середнього з екзогенними змінними. Тому для подальших досліджень рекомендовано використовувати саме цю модель.
За матеріалами магістерської дисертації були написані тези, а також написана наукова стаття. Тези будуть опубліковані в збірці тез доповідей конференції САІТ-2018. А наукова стаття буде опублікована в електронній збірці доповідей у видавництві CEUR.
Прогнозні припущення щодо подальшого розвитку об’єкта дослідження – побудова нових, а також вдосконалення існуючих моделей часових рядів для прогнозування найважливіших характеристик цифрової реклами. А також узагальнення дослідження, що проводилось у даній роботі, на аналіз окремих сайтів із рекламного трафіку.Models for forecasting parameters of digital advertising traffic. Master's thesis: 112 p., 48 fig., 40 tabl., 3 appendixes and 30 sources. The object of study – digital advertising traffic in the form of statistical data. Subject of research – models and methods of analysis of data in the form of time series, methods of applied statistics. Purpose – constructing time series models for forecasting the most important characteristics of digital advertising traffic. Methods of research – time series models for forecasting data and comparative analysis of the obtained models. This paper presents the results of construction of time series models, which are intended for forecasting of the most important characteristics of digital advertising traffic. Described the results of the comparative analysis of the obtained models with the help of information criteria, and also in terms of their accuracy. Was found that for our task, the best model is the ARIMAX model (Autoregressive integrated moving-average model with exogenous inputs). Therefore, it is recommended to use this model for further research. Based on master's dissertation were written theses as well as a scientific article. The theses will be published in the SAIT-2018 conference Book of Abstracts. The scientific article will be published in the electronic collection of reports at the CEUR publishing house (CEUR Workshop Proceedings). The further development of the research object – is the construction of new ones, as well as the improvement of existing time series models for forecasting the most important characteristics of digital advertising traffic. And also – it is a generalization of the research, conducted in this paper, on the analysis of individual sites from the digital advertising traffic.
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Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.
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The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step persistence and the Gaussian process (GP). Root mean squared error (RMSE) is used as the measurement of evaluation and thus determining the accuracy of the models. Results show that the ARIMAX models performed most accurate in every simulation of the single step models implementation, which implies that adding the exogenous predictor wind speed increases the accuracy. However, the accuracy only increased by 0.04% at most, which is determined as a minimal amount. Moreover, the results show that the GP model was 3% more accurate than the multiple step persistence; however, the GP model could be further developed by adding more training data or exogenous variables to the model.
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Cruz, Cristovam Colombo dos Santos. "AnÃlise de sÃries temporais para previsÃo mensal do icms: o caso do PiauÃ." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1648.
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nÃo hÃEsta 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.
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Abalos, Choque Melisa. "Modelo Arima con intervenciones." Universidad Mayor de San Andrés. Programa Cybertesis BOLIVIA, 2009. http://www.cybertesis.umsa.bo:8080/umsa/2009/abalos_cme/html/index-frames.html.
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El desarrollo de gran parte de los modelos y métodos estadísticos, específicamente relacionados con series temporales, ha ido ligado al deseo de estudiar aplicaciones específicas dentro de diversos ámbitos científicos. El presente trabajo también surgió con el objetivo de resolver diversos problemas que se plantean dentro del ámbito econométrico, aunque también puede ser usado en otros ámbitos, todos ellos ligados con un conjunto de datos históricos y con una aplicación muy concreta al estudio del “egreso de divisas” en Bolivia. Se han estudiado a profundidad los modelos para series temporales que únicamente dependían del pasado de la propia serie. En el presente trabajo se inicia el análisis de una serie temporal teniendo en cuenta algún tipo de información externa. En el capítulo 1 se sustenta fuertemente el hecho de investigar acerca de aspectos ajenos a la serie temporal que llegan de algún modo a alterar su normal comportamiento. El capítulo 2 desarrolla minuciosamente modelos univariantes conocidos con el nombre de ARIMA, desarrollando su parte teórica. Posteriormente se complementa esta perspectiva univariante añadiéndose una parte determinística correspondiente al análisis de intervención construyendo así el modelo ARIMA CON INTERVENCIONES, la utilización de éstos modelos es comparada en el capítulo 3, de esta manera se distingui cual de los dos es más efectivo cuando los datos son afectados por eventos circunstanciales. La metodología del modelo ARIMA CON INTERVENCIONES es una herramienta útil para “modelizar” el comportamiento de las series temporales que presentan modificaciones a raíz de eventos ajenos que no pueden ser controlados.
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Örneholm, Filip. "Anomaly Detection in Seasonal ARIMA Models." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388503.
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Almeida, Antonia Fabiana Marques. "AnÃlise Comparativa da AplicaÃÃo de Modelos para ImputaÃÃo do Volume MÃdio DiÃrio de SÃries HistÃricas de Volume de TrÃfego." Universidade Federal do CearÃ, 2010. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7012.
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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel SuperiorPara melhorias do sistema rodoviÃrio, tanto no que se refere à infra-estrutura quanto à operaÃÃo, à necessÃrio a realizaÃÃo de estudos e planejamento, buscando a melhor utilizaÃÃo dos recursos existentes. Para tanto, faz-se o uso de uma importante medida de trÃfego, o volume veicular. Os dados de trÃfego sÃo coletados por meio manuais ou eletrÃnicos, porÃm, ambos podem apresentar falhas e nÃo coletar os dados em sua totalidade. No caso dos equipamentos eletrÃnicos de contagem, a coleta contÃnua pode formar uma sÃrie histÃrica, que, devido a nÃo coleta, gera falhas ao longo da base de dados, as quais podem comprometer os estudos embasados nestas informaÃÃes. Este trabalho busca, portanto, realizar anÃlises de mÃtodos empregados para estimaÃÃo destes valores faltosos, buscando conhecer o modelo mais eficaz para a variÃvel Volume MÃdio DiÃrio dos dados obtidos pelos postos de contagem contÃnua instalados nas rodovias estaduais do CearÃ. Os modelos de estimaÃÃo aplicados neste trabalho sÃo os modelos ARIMA de anÃlise de sÃries temporais, e modelos simples, que apresentam aplicaÃÃo menos complexa e processamento mais rÃpido, enquanto que o ARIMA demanda maior conhecimento especÃfico do profissional que o utiliza. Assim, o mÃtodo mais eficaz aqui considerado foi o que obteve menores erros apÃs aplicaÃÃo do modelo. Para estas aplicaÃÃes foram selecionados quatro postos permanentes, de acordo com o percentual de dados vÃlidos e sua localizaÃÃo, buscando a utilizaÃÃo de postos em pontos representativos do estado. O melhor modelo encontrado foi o ARIMA (1,0,1)7 (com erro mÃdio de 1,816%), porÃm, um dos modelos simples, o MS2, obteve resultados prÃximos aos do ARIMA (erro mÃdio 1,837%), e tambÃm pode ser considerado satisfatÃrio para aplicaÃÃo na imputaÃÃo de valores faltosos.
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 CearÃ. 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.
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Fracaro, Nelize. "Estacionariedade das séries temporais do modelo matemático arimax de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5565.
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Este trabalho científico apresenta o estudo da modelagem matemática de propulsores eletromecânicos das aeronaves tipo multirrotor, uma vez que estes são responsáveis pela estabilidade dessas aeronaves. Esses veículos aéreos de pequeno porte se caracterizam pela ausência física de um controlador. Atualmente, essas naves vêm sendo utilizadas em diversas áreas, para fiscalizar, inspecionar e/ou monitorar através de imagens aéreas e filmagens. Portanto, investiga-se principalmente a estacionariedade das séries temporais da corrente e da velocidade angular do propulsor eletromecânico de forma a obter um modelo matemático não espúrio. Para isso, o estudo visa aprofundar o conhecimento de testes de raiz unitária, os quais são aplicados nos dados coletados das grandezas supracitadas. A metodologia proposta consiste no estudo do sistema de propulsão implementado numa plataforma de testes para a coleta de dados. Posteriormente é realizada a aplicação dos testes de estacionariedade sobre os dados coletados e efetuado o cálculo das funções de autocorrelação e autocorrelação parcial para determinação da estrutura e ordem do modelo. Em seguida é feita a estimação de parâmetros e validação do modelo através da simulação dos dados da plataforma e a análise residual. Após estimados os parâmetros do modelo ARIMAX, foi validado o mesmo pela análise residual e também pelo cálculo do erro, obtendo assim um resultado satisfatório. Com este trabalho auxilia-se a comunidade científica que projeta e desenvolve naves do tipo multirrotor, uma vez que os modelos obtidos dos propulsores eletromecânicos são mais consistentes.88 f.
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Teodoro, Valiana Alves. "Modelos de séries temporais para temperatura em painéis de cimento-madeira." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042015-102815/.
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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.
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naz, saima. "Forecasting daily maximum temperature of Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404.
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The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines. The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI).
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Ballesteros, Lozano Horacio. "Determinación de óptimo de Rolling bajo modelo Arimax para ADR mexicana TMM." Tesis, Universidad de Chile, 2006. http://www.repositorio.uchile.cl/handle/2250/112088.
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Tesis para optar al grado de Magíster en FinanzasNo disponible a texto completo
A través del tiempo tanto las empresas como los mercados enfrentan cada día nuevos retos o desafíos relacionados con demandas estables, competencia intensa, consumidores exigentes y nuevos fenómenos sociales. Estos desafíos requieren en situaciones su previa predicción; debido a esto se han implementado nuevos conceptos y técnicas con el propósito de obtener resultados con mayor eficiencia, disminuyendo la aversión al riesgo para una mejor toma de decisiones. Para el caso de la decisiones financieras las técnicas de pronósticos estadísticos han ayudado a que las personas busquen maneras para poder acceder a mayor información, que les permita poder tomar decisiones de una forma correcta, en donde las posibilidades de equivocarse sean las mínimas y el éxito en la toma de decisiones sea lo más alto posible. La predicción de los fenómenos futuros, están basados en premisas de que los elementos que suceden en la práctica, no son un efecto aleatorio, sino que representan tendencias que podrían ser explicadas de cierta forma por algún modelo; algunas de estas tendencias han servido de mucha ayuda para los inversionistas en sus decisiones. El surgimiento de modelos con comportamiento lineal puede crear cierta certeza en la predicción de resultados, solo que el planteamiento del problema va a ser un elemento clave para lograr una mayor capacidad predictiva junto con la manera de utilizar la información en el modelo
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Santos, Alan Vasconcelos. "AnÃlise de modelos de sÃries temporais para a previsÃo mensal do imposto de renda." Universidade Federal do CearÃ, 2003. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1463.
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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgicoO presente trabalho objetiva realizar previsÃes mensais da sÃrie do imposto de renda para o perÃodo de 2002. A metodologia empregada para alcanÃar essa finalidade consiste na utilizaÃÃo da tÃcnica de combinaÃÃo de previsÃes. Especificamente, combinam-se os resultados de previsÃo advindos de trÃs mÃtodos diferentes: tÃcnica do alisamento exponencial, metodologia de Box-Jenkins (modelos ARIMA) e modelos vetoriais de correÃÃo de erro. Obtida a previsÃo final, compara-se este resultado com os valores reais observados da sÃrie do imposto de renda para o ano de 2002 a fim de verificar o desempenho e a acurÃcia do modelo.
The main objective of this work was to generate predictions, at a monthly frequency, from 1990 to 2001, of income tax revenue. The methodology used was the one of forecast combining. Specifically, exponential smoothing, an ARIMA and VAR with error correction models were pooled to obtain final prediction. Ex-post forecast errors were used to test the performance of the model. Results indicated that combining performs better than individual models, and errors are in an acceptable interval for this type of prediction.
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Isbister, Tim. "Anomaly detection on social media using ARIMA models." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269189.
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This thesis explores whether it is possible to capture communication patterns from web-forums and detect anomalous user behaviour. Data from individuals on web-forums can be downloaded using web-crawlers, and tools as LIWC can make the data meaningful. If user data can be distinguished from white noise, statistical models such as ARIMA can be parametrized to identify the underlying structure and forecast data. It turned out that if enough data is captured, ARIMA models could suggest underlying patterns, therefore anomalous data can be identified. The anomalous data might suggest a change in the users' behaviour.
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Valer, Leila Ana. "Modelo matemático ARIMAX de um propulsor eletromecânico utilizado em naves do tipo multirrotor." reponame:Repositório Institucional da UNIJUI, 2016. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/3628.
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As aeronaves do tipo multirrotor vêm sendo utilizadas como plataforma padrão para
o estudo da motricidade e percepção espacial. A capacidade de decolagem e aterrissagem
de modo vertical, bem como sua navegação horizontal são desafios de investigação na área de controle. Isto demanda a obtenção do modelo matemático do conjunto de propulsão
eletromecânico. Assim, surge a necessidade de compreender e modelar matematicamente
a dinâmica deste sistema de forma a otimizar, posteriormente, o seu controle. Portanto,
o objetivo deste trabalho é obter o modelo matemático do sistema de propulsão eletromecânico, usando para tal a teoria de identificação de sistemas. A metodologia utilizada consiste na compreensão do sistema de propulsão e construção da plataforma de testes para a coleta de dados. Seguida da aplicação de testes de estacionariedade para a análise dos dados, e cálculo das funções de autocorrelação e autocorrelação parcial para determinação da estrutura e ordem do modelo. Posteriormente, os parâmetros são estimados pelo método de mínimos quadrado estendido. Por fim, pela comparação da simulação do modelo com os dados da plataforma e a análise residual, o modelo é validado. Diante disso, conclui-se que o modelo proposto é capaz de descrever as características do sistema de propulsão eletromecânico e poderá contribuir para novas técnicas de controle.111 f.
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Філатова, Ганна Петрівна, Анна Петровна Филатова, and Hanna Petrivna Filatova. "Прогнозування державного боргу з використанням ARIMA моделі." Thesis, ЦФЕНД, 2020. https://essuir.sumdu.edu.ua/handle/123456789/84293.
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Державний борг як важливий фактор соціально-економічного розвитку держави виступає свого роду індикатором і критерієм ефективності провадження виваженої боргової політики держави, а його прогнозування займає одне з ключових місць в процесі забезпечення економічної безпеки держави. У сучасній статистичній теорії існує безліч різноманітних методів прогнозування економічної інформації. Значна їх частина стосується прогнозування часових рядів, без додаткової інформації, тобто без аналізу впливу інших факторів. Звичайно, такий аналіз є доволі неповним, але досить часто результати таких прогнозів є більш точними порівняно з іншими методами прогнозування. Одним з таких методів є побудова ARIMA моделі.
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PENG, SISI. "Evaluating Automatic Model Selection." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.
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In this paper, we briefly describe the automatic model selection which is provided by Autometrics in the PcGive program. The modeler only needs to specify the initial model and the significance level at which to reduce the model. Then, the algorithm does the rest. The properties of Autometrics are discussed. We also explain its background concepts and try to see whether the model selected by the Autometrics can perform well. For a given data set, we use Autometrics to find a “new” model, and then compare the “new” model with a previously selected one by another modeler. It is an interesting issue to see whether Autometrics can also find models which fit better to the given data. As an illustration, we choose three examples. It is true that Autometrics is labor saving and always gives us a parsimonious model. It is really an invaluable instrument for social science. But, we still need more examples to strongly support the idea that Autometrics can find a model which fits the data better, just a few examples in this paper is far from enough.
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Hu, Zhejin. "Time Series Forecasting Model for Chinese Future Marketing Price of Copper and Aluminum." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/60.
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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.
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Post, Eduardo. "Análise dos critérios de erros na validação do modelo matemático Arimax de propulsores eletromecânicos." reponame:Repositório Institucional da UNIJUI, 2018. http://bibliodigital.unijui.edu.br:8080/xmlui/handle/123456789/5526.
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Este trabalho apresenta o estudo da modelagem matemática caixa preta de propulsores eletromecânicos de aeronaves do tipo multirrotor. Estas aeronaves têm sido crescentemente investigadas e ainda estão em evolução. Justifica-se este fato em função de possuírem aplicações em diversas áreas, inclusive em situações que causam risco à vida humana. Dentre os multirrotores destaca-se o quadrirrotor, que vem sendo utilizado como plataforma padrão de estudo. Este possui a capacidade de decolagem e aterrissagem vertical, o que desafia a área de controle. Nesse sentido, estudou-se a modelagem matemática do sistema de propulsão eletromecânico dos multirrotores a fim de poder contribuir futuramente com a otimização de seu controle. A metodologia utilizada consiste na compreensão do sistema de propulsão e utilização de uma plataforma de testes para a coleta de dados, seguida da aplicação de testes de estacionariedade para a análise dos mesmos. O cálculo das funções de autocorrelação e autocorrelação parcial é utilizado para determinação da estrutura e ordem dos modelos matemáticos e posteriormente, os parâmetros são estimados. A validação se dá pela comparação da simulação de cada modelo com os dados da plataforma e a análise dos resíduos. Além disso, são utilizados critérios de informação para seleção de modelos obtendo-se, a partir do Critério de Informação Bayesiano (BIC), uma aproximação prévia de resultados para diferentes modelos, visando garantir possíveis condições impostas pelo projeto que os utilizará. Dessa forma, a metodologia apresentada contribui para novas técnicas de controle.83 f.
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Chmelík, Pavel. "Mají odkupy zbraní pozitivní vliv na míru kriminality?" Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199725.
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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.
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Zhang, Ying, and Hailun Wu. "A comparison of the prediction performances by the linear models and the ARIMA model : Take AUD/JPY as an example." Thesis, Umeå University, Umeå School of Business, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1047.
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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.
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Holens, Gordon Anthony. "Forecasting and selling futures using ARIMA models and a neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq23343.pdf.
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Duarte, Felipe Machado. "Acurácia de previsões para vazão em redes: um comparativo entre ARIMA, GARCH e RNA." Universidade Federal de Pernambuco, 2014. https://repositorio.ufpe.br/handle/123456789/16238.
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Em consequência da evolução da internet, causada por mudanças de paradigma como a Internet das coisas, por exemplo, surgem novas demandas tecnológicas por conta do crescimento do número de dispositivos conectados. Um dos novos desafios que vieram junto a esta demanda é gerenciar esta rede em expansão, de maneira a garantir conectividade aos dispositivos que a integram. Um dos aspectos que merecem atenção no gerenciamento da rede é o provisionamento da largura de banda, que deve ser realizado de maneira a evitar o desperdício de banda, sem por outro lado comprometer a conectividade ao restringi-la demais. No entanto, balancear esta equação não é uma tarefa simples, pois o tráfego de dados na rede é bastante complexo e exibe componentes, como a volatilidade, que tornam difícil a sua modelagem. Já há algum tempo, estudos são publicados apresentando a utilização de ferramentas de análise de séries temporais para prever a vazão de dados em redes de computadores, e entre as técnicas aplicadas com mais sucesso estão os modelos ARMA, GARCH e RNA. Embora estas técnicas tenham sido discutidas como alternativa para modelar dados de tráfego de redes, pouco material está disponível sobre a comparação de suas acurácias, de maneira que neste estudo foi proposta uma avaliação das acurácias dos modelos ARIMA, GARCH e RNA. Esta avaliação foi realizada em cenários configurados em diferentes granularidades de tempo e para múltiplos horizontes de previsão. Para cada um destes cenários foram ajustados modelos ARIMA, GARCH e RNA, e a validação das métricas de acurácia das previsões obtidas se deu através do Rolling Forecast Horizon. Os resultados obtidos mostraram que a RNA exibiu melhor acurácia em grande parte dos cenários propostos, chegando a exibir RMSE até 32% menor que as previsões geradas pelos modelos ARIMA e GARCH. No entanto, na presença de alta volatilidade, o GARCH conseguiu apresentar as previsões com melhor desempenho, exibindo RMSE até 29% menores que os outros modelos estudados. Os resultados deste trabalho servem de auxílio para a área de gerenciamento de redes, em especial a tarefa de provisionamento de largura de banda de tráfego, pois trazem mais informações sobre os desempenhos dos modelos ARIMA, GARCH e RNA ao gerar previsões para este tipo de tráfego.
The Internet evolution, caused by paradigm changes as the Internet of Things, fosters technological advances to cope with the rising number of connected devices. One of the new challenges that appeared with this new reality is the management of such expanding networks, assuring connectivity to every device within them. One of the major aspects of network management is bandwidth provisioning, which must be performed in a way to avoid bandwidth wasting, but without compromising connectivity by restricting it too much. Balancing such an equation is not a simple task, as network data traffic is very complex and presents property features, such as volatility, that turns its modeling rather difficult. It has been some time since research is published with the use of temporal analysis tools to predict data throughput in computer networks, among them, the most successful techniques employ the ARMA, GARCH and ANN models. Although these approaches have been discussed as alternatives do network data traffic modeling, there is little literature available concerning their accuracy, which motivated this work to perform an accuracy evaluation of the ARIMA, GARCH and ANN models. This evaluation was conducted in scenarios configured with different time granularities and for multiple forecast horizons. For each scenario, ARIMA, GARCH and ANN models were set, and the accuracy metrics evaluation was performed with a Rolling Forecast Horizon. Results show that ANN yielded better accuracy in most proposed scenarios, having a RMSE up to 32% lower than the forecasts generated by the ARIMA and GARCH models. However, when there is a high volatility, GARCH provided better forecasts, with a RMSE up to 29% lower than its counterparts. The results from this work provide a useful assistance to network management, especially to bandwidth provisioning, by shedding light on the accuracy presented by the ARIMA, GARCH and ANN models when generating forecasts for this type of traffic.
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Wang, Shuchun. "Exponential Smoothing for Forecasting and Bayesian Validation of Computer Models." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/19753.
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Despite their success and widespread usage in industry and business, ES methods have received little attention from the statistical community. We investigate three types of statistical models that have been found to underpin ES methods. They are ARIMA models, state space models with multiple sources of error (MSOE), and state space models with a single source of error (SSOE). We establish the relationship among the three classes of models and conclude that the class of SSOE state space models is broader than the other two and provides a formal statistical foundation for ES methods. To better understand ES methods, we investigate the behaviors of ES methods for time series generated from different processes. We mainly focus on time series of ARIMA type.
ES methods forecast a time series using only the series own history. To include covariates into ES methods for better forecasting a time series, we propose a new forecasting method, Exponential Smoothing with Covariates (ESCov). ESCov uses an ES method to model what left unexplained in a time series by covariates. We establish the optimality of ESCov, identify SSOE state space models underlying ESCov, and derive analytically the variances of forecasts by ESCov. Empirical studies show that ESCov outperforms ES methods and regression with ARIMA errors. We suggest a model selection procedure for choosing appropriate covariates and ES methods in practice.
Computer models have been commonly used to investigate complex systems for which physical experiments are highly expensive or very time-consuming. Before using a computer model, we need to address an important question ``How well does the computer model represent the real system?" The process of addressing this question is called computer model validation that generally involves the comparison of computer outputs and physical observations. In this thesis, we propose a Bayesian approach to computer model validation. This approach integrates together computer outputs and physical observation to give a better prediction of the real system output. This prediction is then used to validate the computer model. We investigate the impacts of several factors on the performance of the proposed approach and propose a generalization to the proposed approach.
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Pellegrini, Tiago Ribeiro. "Uma avaliação de métodos de previsão aplicados à grandes quantidades de séries temporais univariadas." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/4563.
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Previous issue date: 2012-12-06Financiadora de Estudos e Projetos
Time series forecasting is probably one of the most primordial interests on economics and econometrics, and the literature on this subject is extremely vast. Due to technological growth in recent decades, large amounts of time series are daily collected; which, in a first moment, it requires forecasts according a fixed horizon; and on the second moment the forecasts must be constantly updated, making it impractical to human interaction. Towards this direction, computational procedures that are able to model and return accurate forecasts are required in several research areas. The search for models with high predictive power is an issue that has resulted in a large number of publications in the area of forecasting models. We propose to do a theorical and applied study of forecasting methods applied to multiple univariate time series. The study was based on exponential smoothing via state space approach, automatic ARIMA methods and the generalized Theta method. Each model and method were applied in large data bases of univariate time series and the forecast errors were evaluated. We also propose an approach to estimate the Theta coefficients, as well as a redefinition of the method regarding the number of decomposition lines, extrapolation methods and a combining approach.
A previsão de séries temporais é provavelmente um dos interesses mais primordiais na área de economia e econometria, e a literatura referente a este assunto é extremamente vasta. Devido ao crescimento tecnológico nas últimas décadas, diariamente são geradas e disponibilizadas grandes quantidades de séries temporais; que em um primeiro momento, requerem previsões de acordo com um horizonte fixado; e no segundo momento as previsões precisam ser constantemente atualizadas, tornando pouco prática a interação humana. Desta forma, procedimentos computacionais que modelem e posteriormente retornem previsões acuradas são exigidos em diversas áreas do conhecimento. A busca por modelos com alto poder de preditivo é uma questão que tem resultado em grande quantidade de publicações na área de modelos para previsão. Neste trabalho, propõe-se um estudo teórico e aplicado de métodos de previsão aplicado à múltiplas séries temporais univariadas. O estudo foi baseado em modelos de alisamento exponencial via espaço de estados, método ARIMA automático e o método Theta generalizado. Cada modelo e método foi aplicado em grandes bases de séries temporais univariadas e avaliado o resultado em relação aos erros de previsão. Também foi proposta uma abordagem para estimação dos coeficientes Theta, assim como redefinição do método em relação a quantidade de linhas para decomposição, métodos de extrapolação e combinação das linhas para previsão.
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SILVA, Janilson Alves da. "Estimativa de crescimento em altura de Leucena [Leucaena leucocephala (Lam.) de Wit.] por meio do modelo ARIMA." Universidade Federal Rural de Pernambuco, 2008. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5003.
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The main objective of this work is to use models ARIMA to adjust the estimates of growth in height of leucaena ( emph () Leucaena leucocephala (Lam.) de Wit.) Agreste of Pernambuco. The experiment was conducted at the Experimental Station Company Research Pernambuco Agropecuária - IPA, the municipality of Caruaru - PE. 544 trees were used to leucaena variety, of Hawaii (cv. K8), divided into 24 treatments with 24 repetitions. The sources of variations were: levels of phosphorus, organic compounds and urban waste inoculation with rhizobia (NFB 466 and 473) applied alone. Were considered for this study 5 years of measurements and used the Chapman-Richards model to remove the trend in the series study, after removal of the new trend set S(t) was modeled using models ARIMA (1,1,0), (1,1,1) (1,1,2) and (1,1,3). However, the results were not superior to traditional non-linear models, often used in modeling the growth of forests.
O principal objetivo deste trabalho é utilizar modelos ARIMA para o ajuste das estimativas de crescimento em altura de leucena (Leucaena leucocephala (Lam.) de Wit.), no Agreste de Pernambuco. O experimento foi conduzido na Estação Experimental da Empresa Pernambucana de Pesquisa Agropecuária - IPA, no município de Caruaru - PE. Foram utilizadas 544 árvores de leucena, da variedade Hawaii (cv. K8), divididas em 24 tratamentos com 24 repetições. As fontes de variações estudadas foram: níveis de adubação fosfatada composto orgânico de resíduo urbano e inoculação de rizóbio (NFB 466 e 473) aplicadas isoladamente. Foram consideradas para esse estudo 5 anos de medições e utilizado o Modelo de Chapman-Richards para remover a tendência da série em estudo, após remoção da tendência a nova série St foi modelada utilizando modelos ARIMA (1,1,0);(1,1,1)(1,1,2) e (1,1,3). Entretanto, os resultados não foram superiores aos dos modelos não-lineares tradicionais, frequentemente usados na modelagem do crescimento de florestas.
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Helsing, Johan. "Att prognostisera ungdomsarbetslösheten : En jämförelse av prognosförmågan mellan en ADL-modell och en ARIMA-modell." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37801.
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Qi, Jing. "Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/71.
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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.
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Campos, Celso Vilela Chaves. "Previsão da arrecadação de receitas federais: aplicações de modelos de séries temporais para o estado de São Paulo." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-12052009-150243/.
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O objetivo principal do presente trabalho é oferecer métodos alternativos de previsão da arrecadação tributária federal, baseados em metodologias de séries temporais, inclusive com a utilização de variáveis explicativas, que reflitam a influência do cenário macroeconômico na arrecadação tributária, com o intuito de melhorar a acurácia da previsão da arrecadação. Para tanto, foram aplicadas as metodologias de modelos dinâmicos univariados, multivariados, quais sejam, Função de Transferência, Auto-regressão Vetorial (VAR), VAR com correção de erro (VEC), Equações Simultâneas, e de modelos Estruturais. O trabalho tem abrangência regional e limita-se à análise de três séries mensais da arrecadação, relativas ao Imposto de Importação, Imposto Sobre a Renda das Pessoas Jurídicas e Contribuição para o Financiamento da Seguridade Social - Cofins, no âmbito da jurisdição do estado de São Paulo, no período de 2000 a 2007. Os resultados das previsões dos modelos acima citados são comparados entre si, com a modelagem ARIMA e com o método dos indicadores, atualmente utilizado pela Secretaria da Receita Federal do Brasil (RFB) para previsão anual da arrecadação tributária, por meio da raiz do erro médio quadrático de previsão (RMSE). A redução média do RMSE foi de 42% em relação ao erro cometido pelo método dos indicadores e de 35% em relação à modelagem ARIMA, além da drástica redução do erro anual de previsão. A utilização de metodologias de séries temporais para a previsão da arrecadação de receitas federais mostrou ser uma alternativa viável ao método dos indicadores, contribuindo para previsões mais precisas, tornando-se ferramenta segura de apoio para a tomada de decisões dos gestores.The main objective of this work is to offer alternative methods for federal tax revenue forecasting, based on methodologies of time series, inclusively with the use of explanatory variables, which reflect the influence of the macroeconomic scenario in the tax collection, for the purpose of improving the accuracy of revenues forecasting. Therefore, there were applied the methodologies of univariate dynamic models, multivariate, namely, Transfer Function, Vector Autoregression (VAR), VAR with error correction (VEC), Simultaneous Equations, and Structural Models. The work has a regional scope and it is limited to the analysis of three series of monthly tax collection of the Import Duty, the Income Tax Law over Legal Entities Revenue and the Contribution for the Social Security Financing Cofins, under the jurisdiction of the state of São Paulo in the period from 2000 to 2007. The results of the forecasts from the models above were compared with each other, with the ARIMA moulding and with the indicators method, currently used by the Secretaria da Receita Federal do Brasil (RFB) to annual foresee of the tax collection, through the root mean square error of approximation (RMSE). The average reduction of RMSE was 42% compared to the error committed by the method of indicators and 35% of the ARIMA model, besides the drastic reduction in the annual forecast error. The use of time-series methodologies to forecast the collection of federal revenues has proved to be a viable alternative to the method of indicators, contributing for more accurate predictions, becoming a safe support tool for the managers decision making process.
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Sampaio, Júnior Roberto Antônio de Oliveira. "Modelagem matemática para consciência financeira e a bolsa de valores." reponame:Repositório Institucional da UFABC, 2018.
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Orientador: Prof. Dr. André Ricardo Oliveira da FonsecaDissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Matemática , Santo André, 2018.
O intuito desse trabalho é fomentar o estudo da matemática financeira com o objetivo de um impacto social, para que os alunos de baixa renda atinjam uma consciência financeira maior durante sua formação escolar e construção de sua família. Esse estudo tem motivação pessoal e também éj ustificado pela falta de interesse dos alunos em assuntos de Álgebra, Lógica e Abstração. Através de modelos financeiros da modelagem matemática e de ferramentas computacionais, apresentados na forma de atividades para o Ensino Médio, espera-se uma conscientização maior do aluno em relação à sua liberdade financeira.
The purpose of this work is to promote the study of financial mathematics with the objective of a social impact so that the students of low income achieve a greater financial consistency during their school formation and construction of their family. This study has personal motivation and is also justified by students¿ lack of interest in Algebra, Logic, and Abstraction. Through financial models, mathematical modeling and computational tools, presented in the form of activities for High School, it is expected that students will become more aware of their financial freedom.
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Sans, Fuentes Carles. "Markov Decision Processes and ARIMA models to analyze and predict Ice Hockey player’s performance." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154349.
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In this thesis, player’s performance on ice hockey is modelled to create newmetricsby match and season for players. AD-trees have been used to summarize ice hockey matches using state variables, which combine context and action variables to estimate the impact of each action under that specific state using Markov Decision Processes. With that, an impact measure has been described and four player metrics have been derived by match for regular seasons 2007-2008 and 2008-2009. General analysis has been performed for these metrics and ARIMA models have been used to analyze and predict players performance. The best prediction achieved in the modelling is the mean of the previous matches. The combination of several metrics including the ones created in this thesis could be combined to evaluate player’s performance using salary ranges to indicate whether a player is worth hiring/maintaining/firing
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Becker, Claudia. "Die Re-Analyse von Monitor-Schwellenwerten und die Entwicklung ARIMA-basierter Monitore für die exponentielle Glättung /." Aachen : Shaker, 2006. http://www.gbv.de/dms/zbw/51982640X.pdf.
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Werngren, Simon. "Comparison of different machine learning models for wind turbine power predictions." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362332.
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The goal of this project is to compare different machine learning algorithms ability to predict wind power output 48 hours in advance from earlier power data and meteorological wind speed predictions. Three different models were tested, two autoregressive integrated moving average (ARIMA) models one with exogenous regressors one without and one simple LSTM neural net model. It was found that the ARIMA model with exogenous regressors was the most accurate while also beingrelatively easy to interpret and at 1h 45min 32s had a comparatively short training time. The LSTM was less accurate, harder to interpretand took 14h 3min 5s to train. However the LSTM only took 32.7s to create predictions once the model was trained compared to the 33min13.7s it took for the ARIMA model with exogenous regressors to deploy.Because of this fast deployment time the LSTM might be preferable in certain situations. The ARIMA model without exogenous regressors was significantly less accurate than the other two without significantly improving on the other ARIMA model in any way
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Shakeri, Mohammad Taghi. "Statistical modelling of medical time series data : the dynamic sway magnetometry test." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369783.
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Claudio, Cordeiro Teti Aloisio. "Modelo de previsão da receita tributária : o caso do ICMS no Estado de Pernambuco." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/3786.
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Previous issue date: 2009Esta dissertação tem como principal objetivo apresentar os modelos de previsão de arrecadação do ICMS, por segmento econômico, para a Secretaria da Fazenda do Estado de Pernambuco, utilizando as técnicas econométricas. Objetiva-se, com essa pesquisa, disponibilizar aos gestores púbicos do Estado mais um modelo de previsão consistente e com certo grau de confiabilidade. Para tanto, utilizou-se da metodologia Box-Jenkins, mais especificamente os modelos: ARIMA - modelo autorregressivo integrado de média móvel, e SARIMA - modelo autorregressivo integrado de média móvel sazonal, e o software RATS (Regression Analyse Time Series). O trabalho apresenta o comportamento da arrecadação de ICMS no Estado e uma revisão da literatura, onde são abordados os principais conceitos teóricos utilizados, bem como uma análise dos resultados obtidos. Conclui-se que o modelo de previsão utilizando séries temporais, em função de sua capacidade preditiva, pode se transformar em um valioso instrumento para auxiliar na elevação da receita tributária no Estado de Pernambuco, dentro da capacidade contributiva de cada contribuinte
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Rocha, Neto Augusto. "Previsões para o ICMS no Ceará: comparação do desempenho da metodologia com o modelo ARIMA." reponame:Repositório Institucional da UFC, 2009. http://www.repositorio.ufc.br/handle/riufc/6594.
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ROCHA NETO, Augusto. Previsões para o ICMS no Ceará: comparação do desempenho da metodologia da DEFAZ-CE com o modelo ARIMA. 2009. 42f. Dissertação (mestrado profissional) - Programa de Pós Graduação em Economia, CAEN, Universidade Federal do Ceará, Fortaleza-CE, 2009.Submitted by Mônica Correia Aquino (monicacorreiaaquino@gmail.com) on 2013-11-13T20:21:47Z No. of bitstreams: 1 2009_dissert_arochaneto.pdf: 199258 bytes, checksum: ea6065c13fdd04d85c10839676a5678b (MD5)
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This research aims to offer managers of the State of Ceará a choice of tool to perform estimates of the monthly tax collection Movement of Goods and Services (ICMS), exemplifying the procedures carried out for the period September 2007 to January 2008, through an econometric model consistent with a good predictive power. For this, use is known as univariate ARIMA model, with the use of seasonal dummy, which are very efficient to perform forecasts. The forecasts generated by the research confirms the ability of ARIMA models to forecast opportunity, given the small margin of error, and yet, in view of a comparison between the predictions made by the SEFAZ-CE with those produced by the search, you can say that the model used here is more accurate than the method employed by the Department of Finance to provide for collection of ICMS in Ceará.
Esta pesquisa tem como objetivo oferecer aos gestores do Estado do Ceará uma opção de ferramenta para realizar previsão de arrecadação mensal do Imposto sobre Circulação de Mercadorias e Serviços (ICMS), exemplificando com os procedimentos levados a efeito para o período de setembro de 2007 a janeiro de 2008, por meio de um modelo econométrico consistente e com um bom poder preditivo. Para isso, utilizou-se de modelo univariado conhecido por ARIMA, com o emprego de dummy sazonal, os quais são bastante eficientes para realizar previsões. As previsões geradas pela pesquisa confirmam a capacidade dos modelos ARIMA para ensejar previsão, em virtude da pequena margem de erro; e, ainda, em vista de uma análise comparativa entre as previsões realizadas pela SEFAZ-CE com as produzidas pela pesquisa, pode-se dizer que o modelo aqui utilizado é mais acurados do que o método empregado pela Secretaria da Fazenda para prever arrecadação de ICMS no Ceará.
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Kinene, Alan. "FORECASTING OF THE INFLATION RATES IN UGANDA: : A COMPARISON OF ARIMA, SARIMA AND VECM MODELS." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-49388.
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Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.
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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.
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Miquelluti, Daniel Lima. "Métodos alternativos de previsão de safras agrícolas." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06042015-153838/.
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O setor agrícola é, historicamente, um dos pilares da economia brasileira, e apesar de ter sua importância diminuída com o desenvolvimento do setor industrial e de serviços ainda é responsável por dar dinamismo econômico ao país, bem como garantir a segurança alimentar, auxiliar no controle da inflação e na formação de reservas monetárias. Neste contexto as safras agrícolas exercem grande influência no comportamento do setor e equilíbrio no mercado agrícola. Foram desenvolvidas diversas metodologias de previsão de safra, sendo em sua maioria modelos de simulação de crescimento. Entretanto, recentemente os modelos estatísticos vem sendo utilizados mais comumente devido às suas predições mais rápidas em períodos anteriores à colheita. No presente trabalho foram avaliadas duas destas metodologias, os modelos ARIMA e os Modelos Lineares Dinâmicos (MLD), sendo utilizada tanto a inferência clássica quanto a bayesiana. A avaliação das metodologias deu-se por meio da análise das previsões dos modelos, bem como da facilidade de implementação e poder computacional necessário. As metodologias foram aplicadas a dados de produção de soja para o município de Mamborê-PR, no período de 1980 a 2013, sendo área plantada (ha) e precipitação acumulada (mm) variáveis auxiliares nos modelos de regressão dinâmica. Observou-se que o modelo ARIMA (2,1,0) reparametrizado na forma de um MLD e estimado por meio de máxima verossimilhança, gerou melhores previsões do que aquelas obtidas com o modelo ARIMA(2,1,0) não reparametrizado.The agriculture is, historically, one of Brazil\'s economic pillars, and despite having it\'s importance diminished with the development of the industry and services it still is responsible for giving dynamism to the country inland\'s economy, ensuring food security, controlling inflation and assisting in the formation of monetary reserves. In this context the agricultural crops exercise great influence in the behaviour of the sector and agricultural market balance. Diverse crop forecast methods were developed, most of them being growth simulation models, however, recently the statistical models are being used due to its capability of forecasting early when compared to the other models. In the present thesis two of these methologies were evaluated, ARIMA and Dynamic Linear Models, utilizing both classical and bayesian inference. The forecast accuracy, difficulties in the implementation and computational power were some of the caracteristics utilized to assess model efficiency. The methodologies were applied to Soy production data of Mamborê-PR, in the 1980-2013 period, also noting that planted area (ha) and cumulative precipitation (mm) were auxiliary variables in the dynamic regression. The ARIMA(2,1,0) reparametrized in the DLM form and adjusted through maximum likelihood generated the best forecasts, folowed by the ARIMA(2,1,0) without reparametrization.
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Martins, Gil Gonçalo Freire. "Um novo modelo híbrido multivariado entre uma rede neuronal artificial e um modelo ARIMA para a previsão de taxas de câmbio." Master's thesis, FEUC, 2014. http://hdl.handle.net/10316/25421.
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Trabalho de projeto do mestrado em Economia, apresentado à Faculdade de Economia da Universidade de Coimbra, sob a orientação de Pedro Bação.Apesar dos muitos modelos propostos, é notória a dificuldade na previsão de taxas de câmbio. Estudos recentes com redes neuronais artificiais (RNA) sugerem que estas podem ser uma alternativa a outros modelos para a realização de previsões, quando existem não lineari-dades nas séries temporais. Este estudo propõe uma abordagem multivariada, aplicando um modelo hibrido entre uma RNA e um ARIMA, de forma a aumentar a capacidade de previsão da RNA, dado que uma série temporal pode possuir componente não linear e linear. Imple-mentando primeiro um algoritmo genético adaptativo para construir a estrutura da RNA, são consideradas duas taxas de câmbio mensais e semanais do Euro e do Yen contra o Dólar, para inferir sobre a qualidade do modelo hibrido multivariado proposto face a outros modelos. Os resultados empíricos das previsões fora da amostra, indicam que para as séries mensais o mo-delo hibrido não é vantajoso, devido à não existência de não linearidades nestas. No entanto para séries semanais, obtêm-se vantagens da utilização do modelo hibrido. Assim uma abor-dagem hibrida multivariada pode ser pertinente para a previsão taxas de câmbio semanais e possivelmente diárias, ao invés da utilização dos modelos individuais ou híbridos univariados.
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Palandi, Victor Camillo. "Análise e projeção do ecommerce em Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/22752.
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Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialO consumo online é pauta relevante na sociedade desde o início dos anos 2000. Potencializado pela pandemia global, a importância estratégica deste canal para todos os agentes de mercado é indiscutível. O projeto de pesquisa tem como objetivo apresentar a realidade e evolução do e-commerce em Portugal, a partir da análise de um painel de domicílios, bem como prever a evolução de vendas do canal em 2021. São aplicadas metodologias de alisamento exponencial e modelos de previsão ARIMA de Box-Jenkins a uma base de painel de domicílios concedida pela NielsenIQ - líder mundial em pesquisa de mercado. Conforme espetável, o estudo aponta para uma curva ascendente a nível de vendas do canal até o final de 2021 e deve ser alvo determinante para uma estratégia de sucesso de retalhistas e indústria, bem como uma necessidade latente por parte do consumidor.
Online consumption has been a relevant issue in society since the early 2000s. Powered by the global pandemic, the strategic importance of this channel for all market agents is remarkable. This project aims to present the reality and evolution of e-commerce in Portugal, from the analysis of a panel of households, as well as to predict the evolution of the channel's sales in 2021. Exponential smoothing methodologies and models of Box- Jenkins ARIMA are applied in a household panel database provided by NielsenIQ - world leader in market research. As expected, the study points to an upward curve in the channel's sales by the end of 2021 and should be a key target for a successful strategy for retailers and industry, as well as a latent demand for the consumer.
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Herrmann, Vojtěch. "Moderní predikční metody pro finanční časové řady." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-437908.
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This thesis deals with comparing two approaches to modelling and predicting time series: a traditional one (the ARIMAX model) and a modern one (gradiently boosted decision trees within the framework of the XGBoost library). In the first part of the thesis we introduce the theoretical framework of supervised learning, the ARIMAX model and gradient boosting in the context of decision trees. In the second part we fit the ARIMAX and XGBoost models which both predict a specific time series, the daily volume of the S&P 500 index, which is a crucial task in many branches. After that we compare the results of the two approaches, we describe the advantages of the XGBoost model, which presumably lead to its better results in this specific simulation study and we show the importance of hyperparameter optimization. Afterwards, we compare the practicality of the methods, especially in regards to their computational demands. In the last part of the thesis, a hybrid model theory is derived and algorithms to get the optimal hybrid model are proposed. These algorithms are then used for the mentioned prediction problem. The optimal hybrid model combines ARIMAX and XGBoost models and performs better than each of the individual models on its own. 1
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HUANG, KUO-MING, and 黃國銘. "The Relationehip between Currency ETF and Economic Factors：The Application of ARIMAX-GARCH Model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/f5u85p.
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碩士中原大學
企業管理研究所
104
Recently, the number of financial derivatives have been created to attract the business and public to invest. Among them, the Exchanged-Traded Fund (ETF) is one of the most stable financial derivatives and profitable. In 2005, the first currency ETF (euro IMF) has been established, and it’s performance was affirmed by the international financial markets. The major currencies were used to issue a series of currency ETFs. This study takes the major currencies –such as Australia, United Kingdom, Canada, Switzerland, Japan, USA, New Zealand and other countries as study subjects. The sample data of daily return is from the beginning of January 2007 to the end of December 2015 as the objects. This paper uses the ARIMAX-GARCH model to analze, test and forecast the dynamic relationship of currency ETFs returns and macroeconomic factors. The results are as follows: 1.When volatility index (VIX) rises, the current returns have significantly mixed effect. This shows that the investors may change investment strategies which affected by the previous returns, CRB, stock index and short term interest rate. Therefore, the current returns interacted with investors’s investment strategies may ultimately affect the next-term or few-terms of return. 2.When the current retrrns have upward trend and the previous returns have a negative impact , while CRB, and short-term interest rate showed positice correlation. 3.The results of forecast for the rerurns, reveal that Canada currency ETF''s MAE and RMSE have the minimum value, which means the best predicting performance for the returns and most suitable for investment. The above results indicate the economics factors such as short-term interest rate, stock index, CRB, and VIX having dynamic effects with the currency ETFs. Institution and the investors can take indicators as references, to choose the favor currency ETF based on their investing preference in order to effectively control the returns and avoid the investment risk.
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LIN, CHUN-TENG, and 林浚騰. "Air Polliution Monitoring and Abatement Simulation in Mazu Area - An Application of ARIMAX Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r8899e.
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碩士東海大學
高階經營管理碩士在職專班
107
Taiwan's air quality has been an important issue in recent years, and the most important factor affecting air quality is fine particulate matters(PM2.5). Air pollution, including aerosols(PM10), sulfur dioxide(SO2), nitrogen dioxide (NO2) and carbon monoxide (CO)and Secondary Aerosol. In addition, meteorological factors such as wind speed, temperature and relative humidity are related to the level of primary aerosol, which will also affect the concentration of PM2.5. The purpose of this paper is to estimate and predict the PM2.5 concentration at the Mazu monitoring station. There are many missing values when using the EPA air quality hourly monitoring data. First, use the Back Propagation Neural Network to fill in the missing values; Secondly, the Autoregressive Integrated Moving Average with Explanatory Variable Model(ARIMAX)is used to estimate the regression parameters and the out-of-sample prediction. Finally, the pollution reduction simulation was carried out with an average reduction target of 18 ug/m3. The empirical results show that: (1) the predicted performance of the ARIMAX model, Better than the Autoregressive Integrated Moving Average Model (ARIMA). (2) PM2.5, PM10, NO2, and CO in the first phase will deteriorate PM2.5. (3) During the northeast monsoon period from October to December and March of each year, the average value of PM2.5 reached 23.824 ug/m3; it was significantly higher than the average of other months by 17.839 ug/m3, and there was an additional 2.098 ug/m3 during the day. If the sample average is 20.823 ug/m3, it is found that the foreign pollution in Mazu area is between 20% and 29%. (4) According to the estimation results, we find, on average, the reduction of PM2.5 from 20.823 to 18 per hour mainly depends on the improvement of primary aerosol (about 98%), and followed by CO, NO2 and SO2.
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Cheng, Chih-Yi, and 鄭誌逸. "The Study of the Relationship between Chinese Currency Unification and Macroeocnomic Factors：The Analysis of Fuzzy Neural Network and ARIMAX-GARCH Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/14612548719334102042.
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碩士中原大學
企業管理研究所
96
Refer to the structure of the EURO currency basket, the central rate of Chinese Currency（CCU） Unit was simulated from 1992/3 to the 2007/6 by the weights based on the GDP per capital, the exports, and the net foreign reserve of the Taiwan, Hong Kong, and China. By using each of the Special Drawing Rights（SDR） EURO, and modified-SDR method, the study utilized the analysis of grey relation, fuzzy neural network, and ARIMAX-GARCH model to find out the key factors from import growth rate, export growth rate, trade balance, industrial productive index, foreign reserve, interest rate,, inflation rate, monet supply growth rate, stock index and gross domestic product affecting CCU. According the grey relational analysis by dividing the better five and the worse five factors, the CCU was affected by the industrial productive index, GDP, stock price index, and net foreign reserve. And the fuzzy neutral was used to test forecasting performance for each currency. The research found that the better five variables performed well comparing with the worse five. And the modified-SDR is better than SDR and EURO, except the Hong Kong for EURO method. As analyzing the CCU by fuzzy neutral network, the forecasting performance of Taiwan’s better five variables (gross domestic product, stock index, and industrial productive index, foreign reserve and money supply growth rate) is superior to other groups. If the CCU is simulated by modified-SDR method utilizing the ARIMAX-GARCH model, the forecasting performance of China’s better five variables (industrial productive index, interest rate, export growth rate, stock index, and inflation rate) is the best. If CCU is built by EURO, and the forecasting performance of Hong Kong’s better five variables (industrial productive index, gross domestic product, foreign reserve, trade balance and stock index) has best performance. According to the ARIMAX-GARCH model, the industry productive index, money supply growth rate, and trade factors significantly affect the CCU and its dynamic effect. Generally, the forecasting performance of ARIMAX-GARCH model is better than the neutral network, and the macroeconomic factors effect will be different if the CCU is built by the different way. Finally, this study will provide some valueable suggestions to policy makes and researchers for Taiwan, Hong Kong, and China if the CCU is created in the future.
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Chia, Chi-Te, and 賈繼德. "A study on forecasting models of Taiwan's electricity demand─ARIMA model and Regressive model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/75696372013577266543.
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碩士東吳大學
經濟學系
97
This study discusses the forecasting models of electricity demand in Taiwan, which are ARIMA model and Regression model. From this study, we find that lagged periods are inconsecutive in ARIMA model. For Regression model, it is necessary to cope with variables of different integrated order and autocorrelation of disturbance. We reserve some data as out of sample to test the forecasting accuracy. The result shows that the Regression model is slightly better than the ARIMA model, but the difference is small. By Theil’s inequality coefficient, the forecasting ability of these two models are equal. Besides, we calculate the income elasticity and the price elasticity of electricity. Both are smaller than 1, so electricity is a necessary good, and its price is inelastic.
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Cheng-Liang, Tsai, and 蔡政良. "Forecasting Models of Interest Rate --Using Taylor Rule and ARIMA Model." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/90981861021929838570.
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碩士長庚大學
企業管理研究所
93
In this study, two forecasting models, Taylor Rule and ARIMA, are used to forecast the Fed Funds Rate. In order to compare the accuracy of Taylor Rule and ARIMA, MAE, RMSE and MAPE are also used. By comparing these two forecasting models, this study proposes a reasonable model to forecast the Fed Fund Rate and for individuals and corporations to forecast the rate trend. The sampling data in this study are composed of two components, one is from January 1992 to December 2001, the other is from January 2002 to December 2003. Monthly data of the first component are used to establish forecasting model, and data of the second component are used to inspect the forecasting ability of forecasting model. And the issue of Taylor Rule is separated into two components, which are model I (the original Taylor Rule) and model II (adding new variables in Taylor Rule). The model of Taylor Rule uses co-integrated model and ECM, while the model of ARIMA uses the model identification, parameter estimation, diagnostic checking and forecasting analyzing to determine the forecasting ability of these models. The computational experiments in this study are as follows, the results of MAE, RMSE and MAPE in model I (the original Taylor Rule) are minimum, and the results of MAE, RMSE and MAPE in model II (adding new variables in Taylor Rule) are the second, while the results of MAE, RMSE and MAPE in ARIMA are maximum. In other words, using model I can gain more accurate outcome than model II and the ARIMA model.
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Tseng, Mei-Feng, and 曾美鳳. "Application of ARIMA Model inStock Price Forecasting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vq83np.
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碩士國立臺灣海洋大學
河海工程學系
107
Auto Regression Integrated Moving Average (ARIMA) is often used as an instrument for stock price forecasting. This study aims to take Taiwan Semiconductor Manufacturing Co., Ltd. (TSMC) and Foxconn Technology Group (Foxconn) as examples to apply the ARIMA model for prediction. The models are identified by the goodness of fit and verified by validity indexes to determine the best mode. The results showed that the values of the Root Mean Square Error (RMSE), the Mean Average Error (MAE), and the Mean Absolute Percentage Error (MAPE) are in acceptable ranges. Therefore, the prediction effects of the ARIMA (1,1,1) of TSMC and the ARIMA (2,0,0) of Foxconn modes are acknowledged. When using these modes, in the future, we must notice its applicability and consider the factors of non-stationary state to improve the forecast accuracy of stock prices.
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Ying-Chien, Lu, and 呂英杰. "ARIMA Model , Partial Adjustment Model , and Forecasting of Financial Ratios." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/83421608796147640775.
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碩士國立臺灣科技大學
管理技術研究所
86
Domestically, past research of financial accounting data has focused on forecasting of earnings series over time, but it has been lacking in exploring the time series properties of financial ratio. Therefore, this study relating to the time-series properties of financial ratios is divided into three parts. First, we build the ARIMA and Partial-adjustment models for each financial ratio , discuss the time-series of the financial ratios, and obtain the forecasting values of the two models. Second, we compare the forecasting abilities between the two models. Finally, wedisaggregate the return on assets(ROA) and the return on common equity(ROE) , and test whether the additional information provided by the disaggtegation of the components of the return ratios could improve forecasting results. Quarterly data of ten financial ratios for this study were taken as the overlap among the financial ratios used by the sixteen financial institutions and were obtained from the "REFERENCE ON BANK CREDIT "edited by "CHINA CREDIT INFORMATION SERVICE, LTD ".For a firm to beincluded in the sample, it had to have uniterrupted data for the entire period. This resulted in the inclusion of 85 firms from 15 industries. The period for this study is from 1986/Q2－1997/Q3. The empirical result are as follows :1. Among different industries, the same financial ratios are described by different ARIMA models, and in same of the industries, the financial ratios which belong to the same category tend to be described the same ARIMA model.2. For most sample companies, their financial ratios representing the profit ability (pretax net income ratio, ROA and ROE) are adjusted toward their ideal target ( prior industry mean ) quicker than other financial ratios.3.Of the thirteen industries, the sample data of this analysis are ten quarterly financial ratios of eighty three sample companies. For all of the current ratios, comparing to the prior ratios, the prior industry means (ideal goals) are more interpretative. 4.For the ten quarterly financial ratios in the eighty three companies, the results show that the forecasting ability of the ARIMA model is more accurate than the Partial-Adjustment model.5.For the return on assets, the disaggregating forecasts are more accurate than the aggregating forecasts in six industries including concrete, food, plastic, mechanism, electricity, and chemical industries. For the return on common equity, the disaggregating forecasts are more accurate than the aggregating forecasts in six industries including concrete, food, plastic, mechanism, electricity, and chemical industries.
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Kuo, Yi-Hsiang, and 郭翊翔. "Demand Forecast Model in Wafer Foundry-Analysis by ARIMA Model-." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/58763508773673370642.
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碩士國立交通大學
管理學院碩士在職專班管理科學組
96
In the industrial structure of Taiwan, demand forecast is very critical and necessary for business financial planning, inventory management, manufacturing plan, distribution plan, marketing and customer service and the forecast number is the basic and crucial item for every person on daily operation or meeting. No matter how the forecast number comes from, the only requirement is its accuracy. How it will happen when the accuracy of demand forecast is low to a company? As we can understand, Over-forecast will induce many unnecessary inventories and operation cost. Under-forecast also may have a company lose its opportunities and customer satisfactions. For the reason of that, it is an important topic to improve the accuracy of demand forecast and management the uncertainty of demand. How to manage the uncertainty of demand? Many studies focus on analyzing historical data and using time series forecast model to do demand forecast to management the uncertainty of demand. Hence, the main purpose of this research is to find out a useful ARIMA model by adopting the company’s historical sales data from Jan. 1997 to May 2007. The forecast performance of the ARIMA model in this research is evaluated by MAPE value comparing to the company’s actual sales volume for the following several months (2007.6~2007.12). Based on the MAPE value, the outcome or the forecast performance of this research is very reasonable and accurate. Also, it has better performance than the forecast performance of Sale Division, Regional Planning Division and Headquarter Planning Division in the company. In the real world, it is impossible to have a absolutely precise forecast result. But we can do our best to make the distortion smaller by using forecast models and thus company will improve its competitiveness and gain more profit. Hopefully, this research can be helpful to all the wafer foundries when they are working on demand forecasting and planning.
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Chuang, Han-Ru, and 莊涵如. "Forecasting Short-Term Wind Speed Using ARIMA Model and ANN Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/65976644329930965279.
Full textAbstract:
碩士國立勤益科技大學
工業工程與管理系
101
The purpose of this study is to apply autoregressive integrated moving average (ARIMA) and automated neural networks (ANN) models to predict short-term wind speed. We collected the wind speed in a power plant in Taiwan. In addition to the ARIMA, the ANN is also used to model and predict the wind speed that is collected every ten minutes. The prediction accuracy is determined by mean average error (MAE) and mean relative error (MRE) to determining. We find that both of the MAE and MRE in ANN are smaller than those in the ARIMA. The results show that the ANN model is better than the ARIMA one in forecasting short-term wind speed.
50
Chen, Hung-Shuo, and 陳泓碩. "Runoff Simulation of Fushan Forest Watershed Using ARIMAX and ANFIS Models." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/55207826295533605677.
Full textAbstract:
碩士國立臺灣大學
森林環境暨資源學研究所
100
Rainfall-runoff model is an important issue of hydrological field. In this study, rainfall-runoff models were investigated by applying ARIMAX (ARIMA with exogenous input) and ANFIS (adaptive network-based fuzzy inference system) model. To illustrate the applicapability and capability of these two models in forest watershed, Fushan experimental watershed No.1 was chosen as a case study area. Ten years of daily rainfall and flow data, from 2002 to 2011, were analyzed. There were three types of ARIMAX models developed by 10 years, 5 years and 1 year flow data individually, which are ARIMAX10, ARIMAX5 and ARIMAX1. In the other hand, 15 types of ANFIS model were developed by different data period, membership function and input variables, which are ANFIS110 - ANFIS510, ANFIS15 - ANFIS55 and ANFIS11 - ANFIS51. Results showed that ARIMAX5 model performed well in both simulating and verifying. Also, the best ANFIS model is ANFIS310 model, which was developed by 10 years data from 2002 to 2011, using four input variables: Rt-1, Rt-2, Qt-1, Qt-2 and bell-shaped membership function. ANFIS310 performed well in both simulating and verifying. Besides, the MAE of ARIMAX model is 0.004 - 0.012 m3/sec, RMSE is 0.007 - 0.023 m3/sec, and CE is 86.2 - 93.1%. The MAE of ANFIS model is 0.001 - 0.007 m3/sec, RMSE is 0.003 - 0.031 m3/sec, CE is 74.3 - 98.6 %。All the evaluation indexes of ANFIS model have a larger range than ARIMAX model, because ARIMAX are more stable in simulation and verification on lower flow period. However, ANFIS still can get accurate simulation and verification even on higher flow period, which ARIMAX can’t. In the future, a hybrid model of ARIMAX and ANFIS is a possible method to be applied.