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Mohamed, Fadil B. "Space-time ARIMA and transfer function-noise modeling of rainfall-runoff process." Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4723.
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Fang, Yanhui. "Flood Forecasting via a Combination of Stochastic ARIMA Approach and Deterministic HEC-RAS Modeling." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449142353.
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Landström, Johan, and Patric Linderoth. "Precisionsbaserad analys av trafikprediktion med säsongsbaserad ARIMA-modellering." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14336.
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Intelligenta Transportsystem (ITS) utgör idag en central del i arbetet att försöka höja kvaliteten i transportnätverken, genom att exempelvis ge stöd i arbetet att leda trafik i realtid och att ge trafikanter större möjlighet att ta informerade beslut gällandes sin körning. Kortsiktig prediktion av trafikdata, däribland trafikvolym, spelar en central roll för de tjänster ITS-systemen levererar. Den starka teknologiska utvecklingen de senaste decennierna har bidragit till en ökad möjlighet till att använda datadriven modellering för att utföra kortsiktiga prediktioner av trafikdata. Säsongsbaserad ARIMA (SARIMA) är en av de vanligaste datadrivna modellerna för modellering och predicering av trafikdata, vilken använder mönster i historisk data för att predicera framtida värden. Vid modellering med SARIMA behöver en mängd beslut tas gällandes de data som används till modelleringen. Exempel på sådana beslut är hur stor mängd träningsdata som ska användas, vilka dagar som ska ingå i träningsmängden och vilket aggregationsintervall som ska användas. Därtill utförs nästintill enbart enstegsprediktioner i tidigare studier av SARIMA-modellering av trafikdata, trots att modellen stödjer predicering av flera steg in i framtiden. Besluten gällandes de parametrar som nämnts saknar ofta teoretisk motivering i tidigare studier, samtidigt som det är högst troligt att dessa beslut påverkar träffsäkerheten i prediktionerna. Därför syftar den här studien till att utföra en känslighetsanalys av dessa parametrar, för att undersöka hur olika värden påverkar precisionen vid prediktion av trafikvolym. I studien utvecklades en modell, med vilken data kunde importeras, preprocesseras och sedan modelleras med hjälp av SARIMA. Studien använde trafikvolymdata som insamlats under januari och februari 2014, med hjälp av kameror placerade på riksväg 40 i utkanten av Göteborg. Efter differentiering av data används såväl autokorrelations- och partiell autokorrelationsgrafer som informationskriterier för att definiera lämpliga SARIMA-modeller, med vilka prediktioner kunde göras. Med definierade modeller genomfördes ett experiment, där åtta unika scenarion testades för att undersöka hur prediktionsprecisionen av trafikvolym påverkades av olika mängder träningsdata, vilka dagar som ingick i träningsdata, längden på aggregationsintervallen och hur många tidssteg in i framtiden som predicerades. För utvärdering av träffsäkerheten i prediktionerna användes MAPE, RMSE och MAE. Resultaten som experimentet visar är att definierade SARIMA-modeller klarar att predicera aktuell data med god precision oavsett vilka värden som sattes för de variabler som studerades. Resultaten visade dock indikationer på att en träningsvolym omfattande fem dagar kan generera en modell som ger mer träffsäkra prediktioner än när volymer om 15 eller 30 dagar används, något som kan ha stor praktisk betydelse vid realtidsanalys. Därtill indikerar resultaten att samtliga veckodagar bör ingå i träningsdatasetet när dygnsvis säsongslängd används, att SARIMA-modelleringen hanterar aggregationsintervall om 60 minuter bättre än 30 eller 15 minuter samt att enstegsprediktioner är mer träffsäkra än när horisonter om en eller två dagar används. Studien har enbart fokuserat på inverkan av de fyra parametrarna var för sig och inte om en kombinerad effekt finns att hitta. Det är något som föreslås för framtida studier, liksom att vidare utreda huruvida en mindre träningsvolym kan fortsätta att generera mer träffsäkra prediktioner även för andra perioder under året.Intelligent Transport Systems (ITS) today are a key part of the effort to try to improve the quality of transport networks, for example by supporting the real-time traffic management and giving road users greater opportunity to take informed decisions regarding their driving. Short-term prediction of traffic data, including traffic volume, plays a central role in the services delivered by ITS systems. The strong technological development has contributed to an increased opportunity to use data-driven modeling to perform short-term predictions of traffic data. Seasonal ARIMA (SARIMA) is one of the most common models for modeling and predicting traffic data, which uses patterns in historical data to predict future values. When modeling with SARIMA, a variety of decisions are required regarding he data used. Examples of such decisions are the amount of training data to be used, the days to be included in training data and the aggregation interval to be used. In addition, one-step predictions are performed most often in previous studies of SARIMA modeling of traffic data, although the model supports multi-step prediction into the future. Often, in previous studies, decisions are made concerning mentioned variables without theoretical motivation, while it is highly probable that these decisions affect the accuracy of the predictions. Therefore, this study aims at performing a sensitivity analysis of these parameters to investigate how different values affect the accuracy of traffic volume prediction. The study developed a model with which data could be imported, preprocessed and then modeled using a SARIMA model. Traffic volume data was used, which was collected during January and February 2014, using cameras located on highway 40 on the outskirts of Gothenburg. After differentiation of data, autocorrelation and partial autocorrelation graphs as well as information criteria are used to define appropriate SARIMA models, with which predictions could be made. With defined models, an experiment was conducted in which eight unique scenarios were tested to investigate how the prediction accuracy of traffic volume was influenced by different amount of exercise data, what days was included in training data, length of aggregation intervals, and how many steps into the future were predicted. To evaluate the accuracy of the predictions, MAPE, RMSE and MAE were used. The results of the experiment show that developed SARIMA models are able to predict current data with good precision no matter what values were set for the variables studied. However, the results showed indications that a training volume of five days can generate a model that provides more accurate predictions than when using 15 or 30-day volumes, which can be of great practical importance in real-time analysis. In addition, the results indicate that all weekdays should be included in the training data set when daily seasonality is used, SARIMA modeling handles aggregation intervals of 60 minutes better than 30 or 15 minutes, and that one-step predictions are more accurate than when one or two days horizons are used. The study has focused only on the impact of the four parameters separately and not if a combined effect could be found. Further research is proposed for investigating if combined effects could be found, as well as further investigating whether a lesser training volume can continue to generate more accurate predictions even for other periods of the year.
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Wu, Ling. "Stochastic Modeling and Statistical Analysis." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1813.
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The objective of the present study is to investigate option pricing and forecasting problems in finance. This is achieved by developing stochastic models in the framework of classical modeling approach.
In this study, by utilizing the stock price data, we examine the correctness of the existing Geometric Brownian Motion (GBM) model under standard statistical tests. By recognizing the problems, we attempted to demonstrate the development of modified linear models under different data partitioning processes with or without jumps. Empirical comparisons between the constructed and GBM models are outlined.
By analyzing the residual errors, we observed the nonlinearity in the data set. In order to incorporate this nonlinearity, we further employed the classical model building approach to develop nonlinear stochastic models. Based on the nature of the problems and the knowledge of existing nonlinear models, three different nonlinear stochastic models are proposed. Furthermore, under different data partitioning processes with equal and unequal intervals, a few modified nonlinear models are developed. Again, empirical comparisons between the constructed nonlinear stochastic and GBM models in the context of three data sets are outlined.
Stochastic dynamic models are also used to predict the future dynamic state of processes. This is achieved by modifying the nonlinear stochastic models from constant to time varying coefficients, and then time series models are constructed. Using these constructed time series models, the prediction and comparison problems with the existing time series models are analyzed in the context of three data sets. The study shows that the nonlinear stochastic model 2 with time varying coefficients is robust with respect different data sets.
We derive the option pricing formula in the context of three nonlinear stochastic models with time varying coefficients. The option pricing formula in the frame work of hybrid systems, namely, Hybrid GBM (HGBM) and hybrid nonlinear stochastic models are also initiated.
Finally, based on our initial investigation about the significance of presented nonlinear stochastic models in forecasting and option pricing problems, we propose to continue and further explore our study in the context of nonlinear stochastic hybrid modeling approach.
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Wenzel, Anne. "Komponentenzerlegung des Regelleistungsbedarfs mit Methoden der Zeitreihenanalyse." Master's thesis, Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-66420.
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Im Rahmen der Arbeit wurden die minutengenauen Daten des Regelleistungsbedarfs (Summe aus Sekundärregelleistung und Minutenreserve) der Monate April bis Dezember des Jahres 2009 einer Regelzone einer Zeitreihenanalyse unterzogen und in Komponenten gemäß dem klassischen Komponentenmodell zerlegt. Diese sind die Trendkomponente, ermittelt durch einen gleitenden Durchschnitt mit der Länge einer Stunde, weiterhin zwei periodische Komponenten mit der Periodenlänge einer Stunde sowie der Periodenlänge eines Tages und die Restkomponente, welche mit einem ARIMA(2,1,5)-Prozess modelliert wurde. In der Zukunft sollte das erstellte Modell des Regelleistungsbedarfs durch Hinzunahme einer jahreszeitlichen Komponente noch verbessert werden. Dies war im Rahmen der Arbeit nicht möglich, da keine Daten über einen Zeitraum von mehreren Jahren vorhanden waren. Zusätzlich kann geprüft werden, inwiefern mit dem Komponentenmodell Prognosen durchführbar sind. Dafür sollte die Trendkomponente anders gewählt werden, da sich der hier gewählte Weg zu sehr an den Daten orientiert. Der zweite Teil der Aufgabenstellung dieser Arbeit bestand im Identifizieren inhaltlicher Komponenten, also möglicher Zusammenhänge zwischen dem Regelleistungsbedarf und verschiedenen denkbaren Ursachen. Als potentielle Ursachen wurden der Lastverlauf sowie die Windenergieeinspeisung untersucht. Zwischen der Zeitreihe des Lastverlaufs und der des Regelleistungsbedarfs bestand eine leichte positive Korrelation, zwischen der Zeitreihe der Windenergieeinspeisung und der des Regelleistungsbedarfs eine geringe negative Korrelation.
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Pokhrel, Nawa Raj. "Statistical Analysis and Modeling of Cyber Security and Health Sciences." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7703.
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Being in the era of information technology, importance and applicability of analytical statistical model an interdisciplinary setting in the modern statistics have increased significantly. Conceptually understanding the vulnerabilities in statistical perspective helps to develop the set of modern statistical models and bridges the gap between cybersecurity and abstract statistical /mathematical knowledge. In this dissertation, our primary goal is to develop series of the strong statistical model in software vulnerability in conjunction with Common Vulnerability Scoring System (CVSS) framework. In nutshell, the overall research lies at the intersection of statistical modeling, cybersecurity, and data mining. Furthermore, we generalize the model of software vulnerability to health science particularly in the stomach cancer data.
In the context of cybersecurity, we have applied the well-known Markovian process in the combination of CVSS framework to determine the overall network security risk. The developed model can be used to identify critical nodes in the host access graph where attackers may be most likely to focus. Based on that information, a network administrator can make appropriate, prioritized decisions for system patching. Further, a flexible risk ranking technique is described, where the decisions made by an attacker can be adjusted using a bias factor. The model can be generalized for use with complicated network environments.
We have further proposed a vulnerability analytic prediction model based on linear and non-linear approaches via time series analysis. Using currently available data from National Vulnerability Database (NVD) this study develops and present sets of predictive model by utilizing Auto Regressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) settings. The best model which provides the minimum error rate is selected for prediction of future vulnerabilities.
In addition, we purpose a new philosophy of software vulnerability life cycle. It says that vulnerability saturation is a local phenomenon, and it possesses an increasing cyclic behavior within the software vulnerability life cycle. Based on the new philosophy of software vulnerability life cycle, we purpose new effective differential equation model to predict future software vulnerabilities by utilizing the vulnerability dataset of three major OS: Windows 7, Linux Kernel, and Mac OS X. The proposed analytical model is compared with existing models in terms of fitting and prediction accuracy.
Finally, the predictive model not only applicable to predict future vulnerability but it can be used in the various domain such as engineering, finance, business, health science, and among others. For instance, we extended the idea on health science; to predict the malignant tumor size of stomach cancer as a function of age based on the given historical data from Surveillance Epidemiology and End Results (SEER).
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Simmons, Laurette Poulos. "The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc332047/.
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This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).
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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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AJMAL, KHAN, and MAHMOOD HASHMI TAHIR. "Daily Calls Volume Forecasting." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4852.
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A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two different approaches are used for forecasting the daily call volume Box and Jenkins (ARIMA) methodology and Smoothing methodology. We generate the models for forecasting of call volume and present a comparison of the two different techniques.
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Santana, Delano Mendes de. "Abordagem MRL, Arima e Data Mining para otimização de custos no suprimento energético em plantas petroquímicas." reponame:Repositório Institucional da UFABC, 2018.
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Orientador: Prof. Dr. Douglas Alves CassianoCoorientador: Prof. Dr. Sérgio Ricardo Lourenço
Tese (doutorado) - Universidade Federal do ABC. Programa de Pós-Graduação em Energia, Santo André, 2018.
Uma forma de otimização dos recursos energéticos de uma planta petroquímica é a utilização de Mix Integer Linear Programing (MILP) para decisão da configuração ótima do acionamento dos equipamentos da unidade. Entretanto uma questão ainda em aberto é qual a correlação existente entre a série temporal destes ganhos energéticos com o preço da energia no mercado livre, a temperatura ambiente, a carga da planta e a demanda elétrica desta planta petroquímica. Dessa forma, o objetivo deste trabalho foi obter a correlação entre estas variáveis. A metodologia utilizada contou com três abordagens de exploração de correlações, a primeira foi a Modelagem de Regressão Linear (MRL), a segunda a Autoregressive Integrated Moving Average (ARIMA) e, a terceira, a Data Mining. Como principais resultados foram obtidas as correlações entre estas variáveis pelas três abordagens, além da comparação das regressões em termos de: qualidade de ajuste do modelo; visualização dos dados e aplicação em aplicativos comuns como o Excel®. Adicionalmente foram descobertos padrões escondidos nos dados e gerou-se conhecimento acadêmico capaz de suportar decisões industriais que conduzam a melhorias de eficiência energética.
Is possible to optimize the energy resources of a petrochemical plant using Mix Integer Linear Programing (MILP) to decide the optimal configuration of the equipment. However, a still open question is what correlation exists between the time series of these energy savings with the price of energy in the free market, the ambient temperature, the plant load and the electric demand of this petrochemical plant. The objective of this study is to obtain the correlation between these variables. Three approaches was used, Linear Regression Modeling (LRM), Autoregressive Integrated Moving Average (ARIMA) and Data Mining. Were obtained the correlations between these variables by the three approaches, besides the comparison of the regressions in terms of: adherence to the real values; data visualization and application in common applications like Excel®. In addition, hidden patterns were discovered in the data and academic knowledge was generated, supporting industrial decisions that lead to improvements in energy efficiency.
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Paukeje, Ján. "Analýza a modelování provozu v datových sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219448.
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Theses deals with network traffic modeling focused on elaboration by time series analysis. The nature of network traffic is discussed above all http traffic. First three chapters are theoretical, which describes time series and basic models, linear AR, MA, ARMA, ARIMA and nonlinear ARCH. Other chapters define terms like self-similarity and long range dependence. It is demonstrated a failure of conventional models which cannot capture these specific properties of network data traffic. On the basis of study in chapter 6. is closely described the combined ARIMA/GARCH model and its parameter estimation procedure. Applied part of this theses deals with procedure of estimation and fitting the estimation model to observed network traffic. After an estimation a few future values are predicted on the basis of estimated model. These predicted values are consequently compared with real data.
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Sakli, Leila. "Modélisation et évaluation des vulnérabilités et des risques dans les chaînes logistiques." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4374.
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En dépit de leur caractère distribué, les chaînes logistiques peuvent se révéler très performantes dans les conditions idéales de production et d’échange. Toutefois, leur complexité les rend de plus en plus fragiles. Cette thèse propose des modèles et des méthodes pour l’analyse des risques, de façon à renforcer la robustesse et la résilience des CLs. Nous avons analysé ce domaine suivant une démarche ontologique à l’aide de la méthode KOD pour tirer les caractéristiques essentielles des CLs. En nous appuyant sur un état de l’art du domaine des risques dans les chaînes logistiques, et sur les bases de cas réels, nous avons identifié les indicateurs des vulnérabilités les plus significatifs. A partir des connaissances extraites, et des modèles mathématiques proposés dans la littérature, nous avons construit un modèle de CL multi-étages à l’aide de modèles ARIMA intégrant l’aspect aléatoire de la demande. Pour adapter ce modèle aux situations de vulnérabilité et de risques, nous avons ajouté des contraintes de capacité et de positivité sur les commandes et sur les stocks. Sous l’effet d’événements dangereux, certaines contraintes du système peuvent être atteintes et par conséquence, son évolution peut s’écarter fortement de la dynamique nominale. Nous avons proposé des indicateurs de vulnérabilités comme des indicateurs de fréquence des retards de livraison, ou de surcoût d’immobilisation de produits. Enfin, l’occurrence d’événements dangereux a été représentée par des scénarios. Nous avons alors obtenu des résultats de simulation sous MATLAB, qui nous ont permis d’évaluer leurs conséquences pour différentes configurations du systèmeDespite their distributed nature, these supply chains can be very efficient in the ideal conditions of production and exchange. However, their complexity makes them more fragile. This dissertation proposes models and methods for risk analysis to enhance the robustness and resilience of SCs. We analyzed this area following an ontological approach using the KOD method. Based on state of the art in the field of risk in SCs, and on real cases, we identified the indicators of the most significant vulnerabilities. From the extracted knowledge and mathematical models proposed in the literature, we built the model of a multi-stage SC using ARIMA models incorporating the randomness of the demand. In order to adapt this model to situations of vulnerability and risk, we have added capacity and positivity constraints on orders and inventories. Under the impact of hazardous events or strong disturbances, some constraints of the system can be reached and therefore, its evolution may deviate considerably from the nominal dynamics or even become unstable. We proposed vulnerability indicators such as indicators of the frequency of delivery delays or costs due to the immobilization of products. Finally, scenarios were used to represent the occurrence of dangerous events. We then got simulation results in MATLAB, which allowed us to assess their consequences for different configurations of the system, especially for strong disturbances of information flows and physical flows
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Bajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.
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Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales.Program: Magisterutbildning i informatik
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Pereira, Vera Margarida Vieira Palma. "Modelação e previsão da taxa de inflação de Angola." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10436.
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Mestrado em Econometria Aplicada e PrevisãoO objectivo deste trabalho consiste em comparar os resultados obtidos da previsão das taxas de inflação mensal e homóloga de Angola, usando diferentes modelos (univariados e multivariados) e estratégias. Nos modelos univariados (ARIMA) adoptaram-se quatro estratégias em que a modelação e previsão foram realizadas sobre: os preços das 12 classes que compõem o IPC, na primeira; as variações mensal e homóloga dos preços das 12 classes que compõem o IPC, na segunda; o IPC, na terceira; as variações mensal e homóloga do IPC, na quarta. Nos modelos multivariados (VAR/VEC) utilizaram-se duas estratégias em que a modelação e previsão foram executadas sobre: o IPC, na primeira e as variações mensal e homóloga do IPC, na segunda. Em ambos os casos, foram incluídas variáveis endógenas (oferta de moeda e taxa de câmbio) e exógenas (preço do petróleo, taxa de juro e dummy sazonal) e criados cenários preditivos para a taxa de inflação de Angola. Com a análise realizada, conclui-se que os modelos ARIMA (estratégia 4) apresentaram melhor ajustamento para a previsão da taxa de inflação mensal, em comparação com a taxa de inflação homóloga cujos melhores resultados foram obtidos com os modelos VAR/VEC (estratégia 1)
The main purpose of this document is to compare the predictions obtained on Angola's monthly and homologous (annual) inflation rate, using univariate and multivariate models and strategies. For the univariate models (ARIMA) four strategies were used in which modeling and forecasting were performed on: for the first one, 12 price classes that compose the CPI; for the second one, the monthly and annual price variations of the 12 CPI's classes; for the third one, the CPI; and for the fourth one, the monthly and annual CPI variations. For the multivariate models (VAR/VEC) two strategies were used in which modeling and forecasting were performed on:for the first one, the CPI; and for the second one monthly and annual CPI variations. Both cases included endogenous (money supply and exchange rate) and exogenous variables (oil prices, interest rate) and created predictive scenarios for Angola's inflation rate. As conclusion, ARIMA models (strategy 4) are better tailored to forecast the monthly inflation rate, while VAR/VEC models (strategy 1) gave better results in predicting the annual inflation rate.
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Stavrén, Fredrik, and Nikita Domin. "Modeling of non-maturing deposits." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252302.
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The interest in modeling non-maturing deposits has skyrocketed ever since thefinancial crisis 2008. Not only from a regulatory and legislative perspective,but also from an investment and funding perspective.Modeling of non-maturing deposits is a very broad subject. In this thesis someof the topics within the subject are investigated, where the greatest focus inon the modeling of the deposit volumes. The main objective is to providethe bank with an analysis of the majority of the topics that needs to be cov-ered when modeling non-maturing deposits. This includes short-rate model-ing using Vasicek’s model, deposit rate modeling using a regression approachand a method proposed by Jarrow and Van Deventer, volume modeling usingSARIMA, SARIMAX and a general additive model, a static replicating port-folio based on Maes and Timmerman’s to model the behaviour of the depositaccounts and finally a liquidity risk model that was suggested by Kalkbrenerand Willing. All of these models have been applied on three different accounttypes: private transaction accounts, savings accounts and corporate savingsaccounts.The results are that, due to the current market, the static replicating portfoliodoes not achieve the desired results. Furthermore, the best volume model forthe data provided is a SARIMA model, meaning the effect of the exogenousvariables are seemingly already embedded in the lagged volume. Finally, theliquidity risk results are plausible and thus deemed satisfactory.Intresset för att modellera inlåningsvolymer utan en kontrakterad förfallodaghar ökat markant sedan finanskrisen 2008. Inte bara sett utifrån ett perspek-tiv att uppfylla krav som ställs av tillsynsmyndigheter, men också sett utifrånbankens investerings-och finansieringsperspektiv.Målet med det här arbetet är att förse banken med en analys av majoritetenav de olika områdena som man behöver ta hänsyn till när man ska model-lera inlåningar utan förfallodatum, men med ett fokus på volymmodellering.I den här rapporten modelleras räntor (kortränta och kontoränta), kontovoly-merna, kontobeteendet samt likviditetsrisken. Detta görs med hjälp av Vasicekför korträntan, en regressionsmetod samt en metod som föreslagits av Jarrowoch Van Deventer för kontoräntan, SARIMA, SARIMAX och en generell ad-ditiv regressionsmetod för volymerna, en statisk replikeringsportfölj baseradpå Maes och Timmermans modell för att imitera kontona och slutligen så mo-delleras likviditetsrisken med ett ramverk som föreslagits av Kalkbrener ochWilling. Alla dessa nämnda modeller appliceras, där det är möjligt, på de treolika kontotyperna: privatkonton, sparkonton samt företagssparkonto.Resultatet är att räntemodelleringen samt replikeringsportföljen inte ger ade-kvata resultat på grund av den rådande marknaden. Vidare så ger en SARIMA-modell den bästa prediktionen, vilket gör att slutsatsen är att andra exogenavariabler redan är inneslutna i den fördröjda volymvariabeln. Avslutningsvisså ger likviditetsmodellen tillfredsställande resultat och antas vara rimlig.
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Soki, Johny Roberto. "Métodos de previsão da taxa de inflação de Angola." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/15560.
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Mestrado em Métodos Quantitativos para a Decisão Económica e EmpresarialO objetivo do presente trabalho consiste em comparar os resultados obtidos da modelação e previsão das taxas de inflação mensal e homóloga de Angola, usando diferentes métodos de previsão (estocásticos e determinísticos) e abordagens estratégias. No geral, adotaram-se quatro estratégias em que a modelação e previsão foram realizadas com base nos dados de: (1) séries temporais dos preços das classes que compõem o Índice de Preços no Consumidor (IPC) usando métodos determinísticos de alisamento exponencial; (2) séries temporais dos preços das classes que compõem o IPC usando modelos autorregressivos e de médias móveis (ARMA); (3) série do IPC com a aplicação de modelos ARMA univariados; (4) série do IPC usando modelos ARMA com variáveis exógenas como as taxas de câmbio formal e informal, o preço do barril do petróleo e a oferta de moeda. Com a análise realizada, concluiu-se que o modelo ARMA com variáveis exógenas (estratégia 4) apresenta melhor qualidade preditiva para a previsão quer da taxa de inflação mensal quer da taxa de inflação homóloga de Angola.
The objective of this work is to compare the results obtained from the modeling and prediction of monthly and annual inflation rates in Angola, using different prediction methods (stochastic and deterministic) and strategic approaches. In general, four strategies were adopted in which modeling and forecasting were performed based on data from: (1) time series of prices of the classes that compose the Consumer Price Index (CPI) using deterministic methods of exponential smoothing; (2) time series of prices of the classes that compose the CPI using autoregressive and moving average models (ARMA); (3) IPC series with the application of univariate ARMA models; (4) IPC series using ARMA models with exogenous variables such as official and informal exchange rates, the oil price and the money supply. With the analysis, it was concluded that the ARMA model with exogenous variables (strategy 4) presents a better predictive quality for the forecasting of both the monthly inflation rate and the annual inflation rate of Angola.
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Dal, Santo Paul S. "System identification by ARMA modeling." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23417.
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System identification concerns the mathematical modeling of a system based upon its input and output. It allows the development of a mathematical description when all that is available is the result of a process or the output of a system and not the process or system itself. The purpose of this thesis is to develop algorithms for modeling systems as autoregressive-moving-average processes using the method of instrumental variables, a modification of the ordinary least-squares technique, and a multichannel method based upon processing the input and output data by separate infinite-response filters. The methods developed are tested by computer simulation using several second and third-order test cases and the results are presented
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Guimarães, Rita Cabral Pereira de Castro. "Modelização ARIMA de sucessões cronológicas: aplicação na previsão de escoamentos mensais." Master's thesis, Universidade de Évora, 1997. http://hdl.handle.net/10174/13282.
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Os modelos ARIMA tem vindo a ser cada vez mais utilizados na modelização e previsão de sucessões hidrológicas, instrumento fundamental para o planeamento e gestão de qualquer sistema do domínio Hídrico. A modelização de tais sucessões é conseguida através de uma metodologia em três etapas, desenvolvida por G. E. P. Box e G. M. Jenkins. Deste processo resulta um modelo, considerado como o mais adequado para representar a sucessão, podendo este ser então utilizado na previsão de eventos futuros. Para a aplicação destes modelos utilizaram-se seis sucessões de escoamentos mensais observados em três cursos de água pertencentes à bacia hidrográfica do Rio Douro. A modelização efectuada para esta sucessões permitiu eleger, para cada uma delas, um modelo ARIMA, com o qual se estabeleceram previsões para dois anos consecutivos à última observação. / Abstract - ARIMA models have become an important tool for modelling and forecasting of hydrologic sequences. Theses techniques are of considerable importance to the design and operation of water resource systems. Before being able to forecasting future values, models have to be found which describe past data adequately. These is accomplished with a iterative process, developed by G. E. P. Box and G. M. Jenkins, which incorporates three stages. For the applications of these models we selected six monthly flow sequences for three rivers located in Douro River watershed. The modelling of such sequences gave one ARIMA model for the forecasting of flows two years ahead.
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Conway, Eunan Martin. "Stochastic modelling and forecasting of solar radiation." Thesis, Northumbria University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367414.
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Li, Tracy Xiaoping. "ARMA lattice modeling for isolated word speech recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0012/MQ52599.pdf.
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Velasco, Solano Carlos Hernando. "ARMA modeling of signals in the time domain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23820.
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Approved for public release; distribution is unlimitedThis thesis develops an iterative algorithm for the design of ARMA models of signals in the time domain. The algorithm is based on optimization techniques, particularly a gradient technique known as the restricted step method is used. The new algorithm is called the iterative Prony method, and the results obtained using this new method are compared to those obtained using the iterative prefiltering algorithm. The thesis shows that the performance of the iterative Prony method is in most of the cases comparable or superior to that of the iterative prefiltering algorithm.
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Avventi, Enrico, Anders Lindquist, and Bo Wahlberg. "ARMA Identification of Graphical Models." KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-39065.
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Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data.Updated from "Preprint" to "Article" QC 20130627
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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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Taherifard, Ershad. "Load and Demand Forecasting in Iraqi Kurdistan using Time series modelling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260260.
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This thesis examines the concept of time series forecasting. More specifically, it predicts the load and power demand in Sulaymaniyah, Iraqi Kurdistan, who are today experiencing frequent power shortages. This study applies a commonly used time series model, the autoregressive integrated moving average model, which is compared to the naïve method. Several key model properties are inspected to evaluate model accuracy. The model is then used to forecast the load and the demand on a daily, weekly and monthly basis. The forecasts are evaluated by examining the residual metrics. Furthermore, the quantitative results and the answers collected from interviews are used as a basis to investigate the conditions of capacity planning in order to determine a suitable strategy to minimize the unserved power demand. The findings indicate an unsustainable over consumption of power in the region due to low tariffs and subsidized energy. A suggested solution is to manage power demand by implementing better strategies such as increasing tariffs and to use demand forecast to supply power accordingly. The monthly supply forecast in this study outperforms the baseline method but not the demand forecast. On weekly basis, both the load and the demand models underperform. The performance of the daily forecasts performs equally or worse than the baseline. Overall, the supply predictions are more precise than the demand predictions. However, there is room for improvement regarding the forecasts. For instance, better model selection and data preparation can result in more accurate forecasts.Denna studie undersöker prediktion av tidserier. Den tittar närmare på last- och effektbehov i Sulaymaniyah i Irak som idag drabbas av regelbunden effektbrist. Rapporten applicerar en vedertagen tidseriemodell, den autoregressiva integrerade glidande medelvärdesmodellen, som sedan jämförs med den naiva metoden. Några karaktäristiska modellegenskaper undersöks för att evaluera modellens noggrannhet. Den anpassade modellen används sedan för att predikera last- och effektbehovet på dags-, månads-, och årsbasis. Prognoserna evalueras genom att undersöka dess residualer. Vidare så användas de kvalitativa svaren från intervjuerna som underlag för att undersöka förutsättningarna för kapacitetsplanering och den strategi som är bäst lämpad för att möta effektbristen. Studien visar att det råder en ohållbar överkonsumtion av energi i regionen som konsekvens av låga elavgifter och subventionerad energi. En föreslagen lösning är att hantera efterfrågan genom att implementera strategier som att höja elavgifter men även försöka matcha produktionen med efterfrågan med hjälp av prognoser. De månadsvisa prognoserna för produktionen i studien överträffar den naiva metoden men inte för prognoserna för efterfrågan. På veckobasis underpresterar båda modellerna. De dagliga prognoserna presterar lika bra eller värre än den naiva metoden. I sin helhet lyckas modellerna förutspå utbudet bättre än efterfrågan på effekt. Men det finns utrymme för förbättringar. Det går nog att uppnå bättre resultat genom bättre förbehandling av data och noggrannare valda tidseriemodeller.
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Willersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.
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The 1st and 2nd order Lee–Carter methods were compared with the Hyndman–Ullah method in regards to goodness of fit and forecasting ability of mortality rates. Swedish population data was used from the Human Mortality Database. The robust estimation property of the Hyndman–Ullah method was also tested with inclusion of the Spanish flu and a hypothetical scenario of the COVID-19 pandemic. After having presented the three methods and making several comparisons between the methods, it is concluded that the Hyndman–Ullah method is overall superior among the three methods with the implementation of the chosen dataset. Its robust estimation of mortality shocks could also be confirmed.
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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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Leser, Christoph. "On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/29319.
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The concise description of one- and multidimensional stationary and non stationary vehicle loading histories for fatigue analysis using stochastic process theory is presented in this study. The load history is considered to have stationary random and nonstationary mean and variance content. The stationary variations are represented by a class of time series referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is used to account for the variation of the mean and variance. Due to the use of random phase angles in the Fourier series, an ensemble of mean and variance variations is obtained. The methods of nonparametric statistics are used to determine the success of the modeling of nonstationarity. Justification of the method is obtained through comparison of rainflow cycle distributions and resulting fatigue lives of original and simulated loadings. Due to the relatively small number of Fourier coefficients needed together with the use of ARMA models, a concise description of complex loadings is achieved. The overall frequency content and sequential information of the load history is statistically preserved. An ensemble of load histories can be constructed on-line with minimal computer storage capacity as used in testing equipment. The method can be used in a diversity of fields where a concise representation of random loadings is desired.Ph. D.
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Paulionienė, Laura. "Statistical modelling of spatio-temporal data based on spatial interpolation of time series parameters." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140117_113114-31261.
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Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu.
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Lee, Ming-Tsung [Verfasser], and Bernhard [Akademischer Betreuer] Friedrich. "Short-term Freeway Traffic Flow Forecasting with ARIMAX Modeling / Ming-Tsung Lee ; Betreuer: Bernhard Friedrich." Braunschweig : Technische Universität Braunschweig, 2010. http://d-nb.info/1175827878/34.
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Alimi, Aria [Verfasser]. "Modeling and Numerical Simulation of Fluid-Structure Interaction in Circle of Willis / Aria Alimi." Kassel : Kassel University Press, 2019. http://d-nb.info/1206954078/34.
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Fonseca, Ronald Bernardes. "Modelling main worldwide financial Ãndices risk management: so far, but so close!" Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15526.
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nÃo hÃO presente artigo busca uma mÃtrica refinada e confiÃvel para mensurar riscos financeiros. RiskMetrics (1994) marcou o inÃcio dessa busca e desde entÃo vÃrios pesquisadores contribuÃram com inovaÃÃes e novos modelos para essa medida e aqui se apresenta mais um passo desse caminho, ao se agregar uma modelagem multivariada. Com essa modelagem à possÃvel capturar o efeito contÃgio e a interdependÃncia financeira global. O grupo de 10 paÃses presente no estudo representa 49,9% do PIB mundial e possuem representantes de 5 continentes. O modelo de volatilidade segue sugestÃo apresentada por Cappielo, Engle e Sheppard (2006) e modelos de Value-at-Risk (VaR) seguem Matos, Cruz, Macedo e Jucà (CAEN-UFC Workingpaper). AtravÃs desse procedimento à possÃvel calcular VaR levando em consideraÃÃo o efeito contÃgio e a interdependÃncia entre os mercados ao longo do tempo. Os resultados encontrados sÃo robustos contra problemas de variÃveis omitidas, heterocedasticidade e endogeneidade, alÃm de considerar quebras estruturais. De acordo com os resultados encontrados, a interdependÃncia apresenta um papel importante dentro do processo de mensuraÃÃo de risco de mercado, apesar de atà agora ter sido esquecida pelos pesquisadores. Isso se deve, principalmente, porque a integraÃÃo financeira a nÃvel global leva ao cenÃrio de dependÃncia crescente entre os mercados financeiros e, dessa forma, aumentando o contÃgio de um impacto que ocorre em um mercado nos outros. Convidamos outros pesquisadores a rever nossa metodologia, utilizando inclusive mais informaÃÃes e incluindo outros paÃses. Acredita-se que o mundo està ano a ano se tornando mais globalizado e suas economias por consequÃncia. Nesse artigo esse efeito està sendo considerado dentro da mensuraÃÃo do risco de mercado. Incorporar esse efeito leva a modelagem, legal e interna, mais acurada, que ajuda supervisores de mercado a garantirem estabilidade de longo prazo para os mercados e possuÃrem mÃtricas mais confiÃveis dentro das instituiÃÃes sob sua tutela. AlÃm disso, à de grande valia para Ãreas de GestÃo de Risco de bancos e instituiÃÃes financeiras ao ajuda-las a compreender melhor seu perfil de risco, melhorar a comunicaÃÃo com investidores institucionais internacionais e ranquear de maneira mais eficiente seus investimentos e aplicaÃÃes. Estudos anteriores possuem um aspecto comum: Apenas levam em consideraÃÃo mudanÃas de volatilidade nos mercados domÃsticos, nÃo levando em consideraÃÃo os efeitos que outros paÃses possuem neles. No presente estudo, esse efeito se provou como importante e representativo, os modelos univariados domÃsticos falharam mais e com mais severidade que os modelos multivariados. Portanto, no presente artigo, buscou-se o desafio de dar o passo de nÃo mais modelar modelos univariados domÃsticos, mas modelos 4 multivariados globais. Acredita-se que esse avanÃo metodolÃgico ajudarà a melhor mensurar e entender o comportamento do risco de mercado atravÃs do mundo.
This paper enter into the search of a refined and trustable metric for measuring financial risk. RiskMetrics (1994) marked the start of this search and since them many researches contributed with innovations and new models for that measure, and here we find a stepforward into the search, by aggregating multivariate models, with this itâs possible to capture the effect of a worldwide contagion and financial interdependence. The group of 10 countries presents in this study represents 49,9% of world GDP and has representation across 5 continents. We follow the model of volatilities suggested in Cappielo, Engle e Sheppard (2006) and Value-at-Risk follows Matos, Cruz, Macedo e Jucà (CAEN-UFC Working paper), though this procedure itâs possible to accurate VaR model, and take in count the contagion and interdependence between markets, in long term. Our results are robust to problems with omitted variable, heteroskedasticity and endogeneity. We also take into account for structural break. According to our results, the interdependence plays an important role into financial risk measure process, although its until now usually forbidden by modelers, mostly because worldâs financial integration leads the global economies to the scenario of increasing dependence among them and contagion effect that spreads the impacts that occur into one market to the others. We invite researchers to revisit this issue in order obtain evidences using larger data and other countries as well. We claim that the world is year by year more globalized, and so are the other economies, here we add this into account for measuring financial risks. This leads to model, legal and internal, more accurate that help supervisors to guarantee the long term stability across the markets, have trustable measure of the financial institutions under their responsibility. Besides, helps the Risk Management area of banks and other financial institutions to better understand their risk profile, improve communication with institutional investors worldwide and rank effiently their investments and applications into the markets. Previous studies have a common aspect: they only consider the volatilities change across the domestic market, not tanking in consider the effect of the other countries into the domestic volatility, and this effect here is proven to be important and representative, the univariate domestic risk measure fails more and harder than the multivariate model. That being said, here we take this step, the challenge of modeling no more univariate, domestic risk measures, but a worldwide multivariate. This is a methodological innovation that helps better measure and understands the financial risks behavior across the world.
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Huang, Xiaoyan. "Predicting Short-Term Exchange Rates with a Hybrid PPP/UIP Model." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/236.
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This study creates a model to predict short-term exchange rates as a combination of the relative purchasing power parity model (Grossman and Simpson 2011) and the interest power parity model. I then use the statistical techniques ARMA and GARCH to account for the variance of the terms. Previous works considered the effects of these models individually, but mine consider them in unison. I consider both in-sample and out-of-sample tests. I use data on five major exchange rates (JPY/USD, CAD/USD, CHF/USD, GBP/USD, and AUD/USD) sampled at a monthly frequency from 1989-2013. My model statistically significantly predicts these exchange rates over the January 2012 to January 2013 period.
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Luo, Xiaoguang [Verfasser], and B. [Akademischer Betreuer] Heck. "GPS Stochastic Modelling - Signal Quality Measures and ARMA Processes / Xiaoguang Luo. Betreuer: B. Heck." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1027531008/34.
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Karlsson, Erlendur. "Least squares arma modeling of linear time-varying systems : lattice filter structures and fast RLS algorithms." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/15936.
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Malmgren, Erik, and Annie Zhang. "Risk Modeling of Sustainable Mutual Funds Using GARCH Time Series." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273578.
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The demand for sustainable investments has seen an increase in recent years. There is considerable literature covering backtesting of the performance and risk of socially responsible investments (SRI) compared to conventional investments. However, literature that models and examines the risk characteristics of SRI compared to conventional investments is limited. This thesis seeks to model and compare the risk of mutual funds scoring in the top 10% in terms of sustainability, based on Morningstar Portfolio Sustainability Score, to those scoring in the bottom 10%. We create one portfolio consisting of the top 10% funds and one portfolio consisting of the bottom 10%, for European and global mutual funds separately, thus in total creating 4 portfolios. The analysis is based on data of the funds' returns and Morningstar Portfolio Sustainability Scores during December 2015 to August 2019. Investigating several GARCH models, we find an ARMA-GARCH model with skewed Student's t-distribution as innovation distribution to give the best fit to the daily log-returns of each portfolio. Based on the fitted ARMA-GARCH models with skewed Student's t-distribution, we use a parametric bootstrap method to compute 95% confidence intervals for the difference in long-run volatility and value at risk (VaR) between the portfolios with high and low Morningstar Portfolio Sustainability Scores. This is performed on the portfolios of European and global funds separately. We conclude that, for global and European funds respectively, no significant difference in terms of long-run volatility and VaR is found between the funds in each of the 10% ends of the Morningstar Portfolio Sustainability Score.Efterfrågan av hållbara investeringar har ökat kraftigt de senaste åren. Det finns många studier som genomför backtesting av hållbara investeringars avkastning och risk jämfört med konventionella investeringar. Färre studier har däremot gjorts för att modellera och jämföra investeringarnas riskegenskaper. Denna uppsats syftar till att modellera risken av hållbara investeringar genom att jämföra de 10% fonder med högst Morningstar Portfolio Sustainability Score mot de 10% fonder med lägst score. Jämförelsen görs separat för globala fonder och europeiska fonder, vilket resulterar i totalt 4 portföljer. Analysen baseras på data på fondernas avkasting och Morningstar Portfolio Sustainability Score under tidsperioden december 2015 till augusti 2019. Genom att undersöka flera olika GARCH-modeller, kommer vi fram till att en ARMA-GARCH-modell med skev t-fördelning bäst beskriver den dagliga logaritmerade avkastningen för varje portfölj. Baserat på de anpassade ARMA-GARCH-modellerna, används en "parametric bootstrap"-metod för att beräkna 95%-iga konfidensintervall för skillnaden i långsiktig volatilitet och value at risk (VaR) mellan portföljerna med högt och lågt Morningstar Portfolio Sustainability Score. Detta görs separat för de europeiska och globala fonderna. Vår slutsats är att det, för globala och europeiska fonder, inte råder en signifikant skillnad i långsiktig volatilitet eller VaR mellan fonder med högt och lågt Morningstar Portfolio Sustainability Score.
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Veith, Susanne R., France M. Berruex, Eric Hughes, and Sotiris E. Pratsinis. "Modelling aroma release from silica Sol-Gel particles using self-diffusion data obtained under magic angle spinning conditions." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-194915.
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Veith, Susanne R., France M. Berruex, Eric Hughes, and Sotiris E. Pratsinis. "Modelling aroma release from silica Sol-Gel particles using self-diffusion data obtained under magic angle spinning conditions." Diffusion fundamentals 3 (2005) 27, S. 1-2, 2005. https://ul.qucosa.de/id/qucosa%3A14318.
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Lennon, Hannah. "Gaussian copula modelling for integer-valued time series." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/gaussian-copula-modelling-for-integervalued-time-series(fff45515-19a4-4063-8ad2-4f9aac4017cb).html.
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This thesis is concerned with the modelling of integer-valued time series. The data naturally occurs in various areas whenever a number of events are observed over time. The model considered in this study consists of a Gaussian copula with autoregressive-moving average (ARMA) dependence and discrete margins that can be specified, unspecified, with or without covariates. It can be interpreted as a 'digitised' ARMA model. An ARMA model is used for the latent process so that well-established methods in time series analysis can be used. Still the computation of the log-likelihood poses many problems because it is the sum of 2^N terms involving the Gaussian cumulative distribution function when N is the length of the time series. We consider an Monte Carlo Expectation-Maximisation (MCEM) algorithm for the maximum likelihood estimation of the model which works well for small to moderate N. Then an Approximate Bayesian Computation (ABC) method is developed to take advantage of the fact that data can be simulated easily from an ARMA model and digitised. A spectral comparison method is used in the rejection-acceptance step. This is shown to work well for large N. Finally we write the model in an R-vine copula representation and use a sequential algorithm for the computation of the log-likelihood. We evaluate the score and Hessian of the log-likelihood and give analytic solutions for the standard errors. The proposed methodologies are illustrated using simulation studies and highlight the advantages of incorporating classic ideas from time series analysis into modern methods of model fitting. For illustration we compare the three methods on US polio incidence data (Zeger, 1988) and we discuss their relative merits.
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Dahlen, Anders. "Identification of stochastic systems : Subspace methods and covariance extension." Doctoral thesis, KTH, Mathematics, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.
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Gonçalves, Daniel. "Citrus essential oil fractionation using ethanol with different water contents as solvents: phase equilibrium, physical properties and continuous equipment extraction." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/74/74132/tde-22092017-150246/.
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Essential oils are featured commodities in the global market due their many applications in food and chemical industries, in different medicine areas, and as antibacterial, antifungal, and antioxidant agent. One of the phenomena accountable for the loss of essential oils quality may be associated with the degradation of terpene hydrocarbons by oxidation when exposed to air, light or heat, causing disagreeable odors. The procedure of terpenes content reducing, known as deterpenation, can be performed by diverse techniques, among which the liquid-liquid extraction can be highlighted since this process can be operated without the use of heating and pressure changes, causing low impact on the essential oil sensory qualities and low energy consumption. This research was focused on the fractionation process of citrus essential oils, by liquid-liquid extraction, using ethanol/water mixtures as solvents. Experimental liquid-liquid equilibrium data of model and real citrus systems were obtained. The aroma profile of the crude citrus essential oils (orange - Citrus sinensis and lime - Citrus aurantifolia) and the phases from the liquid-liquid equilibrium was also evaluated. Moreover, the crude citrus essential oils were fractionated in a continuous operation equipment (perforated rotating disc contactor, PRDC). It was verified that the water has an important influence over the fractionation performance, but not over the aroma profile of the phases. The experimental data from the model systems (citrus essential oil model mixture + ethanol + water) were used to adjust parameters of empirical and thermodynamic models, which provided satisfactory results on the calculation of physical property values and compositions of the phases from the real systems (crude citrus essential oil + ethanol + water). The fractionation of citrus essential oils by liquid-liquid extraction technology was technically feasible and can be accomplished into continuous apparatus such as PRDC column. The solvents employed provided extract phases enriched in oxygenated compounds.Óleos essenciais são importantes produtos comercializados mundialmente devido às suas diversas aplicações em indústrias alimentícias e químicas, em diferentes áreas da medicina, e como agentes antibacteriano, antifúngico e antioxidante. Um dos fenômenos responsáveis pela sua perda de qualidade pode estar associado à degradação dos hidrocarbonetos terpênicos por oxidação, quando estes são expostos ao ar, luz ou calor, ocasionando odor desagradável. O procedimento para redução do teor de terpenos no óleo essencial, conhecido como desterpenação, pode ser realizado por diferentes técnicas, entre as quais a extração líquido-líquido se destaca uma vez que pode ser conduzida sem o emprego de calor e mudanças na pressão, o que atenua o impacto nas qualidades sensoriais e demanda menor gasto energético. Este estudo se concentrou no processo de fracionamento de óleos essenciais cítricos, pela técnica de extração líquido-líquido, empregando misturas de etanol e água como solventes. Foram obtidos dados de equilíbrio líquido-líquido de sistemas cítricos modelo e reais. O perfil de aroma dos óleos essenciais brutos (laranja - Citrus sinensis e lima ácida Citrus latifolia) e das fases provenientes do equilíbrio líquido-líquido também foram avaliados. Além disso, os óleos essenciais brutos foram submetidos ao processo de fracionamento em equipamento de operação contínua (coluna de discos rotativos perfurados, PRDC). Verificou-se que a água possui uma importante influência sobre o desempenho do processo de fracionamento, mas não afetou o aroma das fases. Os dados experimentais dos sistemas modelo (mistura modelo de óleo essencial cítrico + etanol + água) foram utilizados para o ajuste de parâmetros de modelos empíricos e termodinâmicos, os quais apresentaram bons resultados no cálculo de valores de propriedades físicas e da composição das fases oriundas dos sistemas reais (óleo essencial cítrico bruto + etanol + água). O fracionamento de óleos essenciais cítricos pela tecnologia de extração líquido-líquido mostrou-se tecnicamente viável e pode ser conduzido em equipamentos contínuos como a coluna de extração PRDC. Os solventes empregados permitiram a obtenção de fases extrato enriquecidas com compostos oxigenados.
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Chen, Liang. "Small population bias and sampling effects in stochastic mortality modelling." Thesis, Heriot-Watt University, 2017. http://hdl.handle.net/10399/3372.
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Pension schemes are facing more difficulties on matching their underlying liabilities with assets, mainly due to faster mortality improvements for their underlying populations, better environments and medical treatments and historically low interest rates. Given most of the pension schemes are relatively much smaller than the national population, modelling and forecasting the small populations' longevity risk become urgent tasks for both the industrial practitioners and academic researchers. This thesis starts with a systematic analysis on the influence of population size on the uncertainties of mortality estimates and forecasts with a stochastic mortality model, based on a parametric bootstrap methodology with England and Wales males as our benchmark population. The population size has significant effect on the uncertainty of mortality estimates and forecasts. The volatilities of small populations are over-estimated by the maximum likelihood estimators. A Bayesian model is developed to improve the estimation of the volatilities and the predictions of mortality rates for the small populations by employing the information of larger population with informative prior distributions. The new model is validated with the simulated small death scenarios. The Bayesian methodologies generate smoothed estimations for the mortality rates. Moreover, a methodology is introduced to use the information of large population for obtaining unbiased volatilities estimations given the underlying prior settings. At last, an empirical study is carried out based on the Scotland mortality dataset.
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Morakul, Sumallika. "Etude et modélisation de la composition du gaz fermentaire en conditions œnologiques : intéret pour le controle de la fermentation." Thesis, Montpellier, SupAgro, 2011. http://www.theses.fr/2011NSAM0005/document.
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Ce travail décrit les équilibres gaz-liquide lors des fermentations alcooliques en conditions œnologiques, en se focalisant sur les composés d'arôme les plus abondants : isobutanol, alcool isoamylique, acétate d'éthyle, acétate d'isoamyle et hexanoate d'éthyle. Les coefficients de partage à l'équilibre (ki) de ces molécules sont quantifiés, grâce à la méthode PRV (Phase Ratio Variation), en précisant l'effet de la composition du milieu et de la température. Grâce à la mise en œuvre de fermentations spécifiques pendant lesquelles la vitesse de dégagement de CO2 est contrôlée (grâce à une perfusion d'azote ammoniacal), il a été montré que le dégagement de CO2 n'avait pas d'effet sur le rapport de concentrations entre phase gaz et liquide et pouvait être assimilé au ki. Une démarche de modélisation est ensuite effectuée, pour estimer la valeur du rapport de concentrations entre phase liquide et gaz pour ces composés (à l'exception de l'acétate d'éthyle et de l'alcool isoamylique), à tout moment de la fermentation et quelle que soit la température, y compris en conditions de non isothermie. Le modèle, basé sur l'équilibre des phases gaz et liquide, conduit à une erreur moyenne d'estimation inférieure à 10%. Grâce à ce modèle, il devient possible de réaliser des bilans de production des différents composés tout au long de la fermentation, à partir de leur seule mesure dans la phase gaz. Ces bilans permettent de différencier (i) la quantité globale produite, représentative des potentialités de la levure (intérêt microbiologique), (ii) la quantité restant dans la phase liquide (intérêt organoleptique) et (ii) la quantité perdue dans le gaz effluent (intérêt technologique). Il est ainsi mis en évidence que les pertes d'esters sont très importantes. Par exemple, à 20°C, elles atteignent respectivement 44 % et 25 % pour l'hexanoate d'éthyle et l'acétate d'isoamyle. Grâce à un dispositif de suivi en ligne permettant des mesures très fréquentes (une par heure), les vitesses - et les vitesses spécifiques - de production et de pertes sont calculées. Elles constituent de nouvelles informations utiles à la fois pour l'étude du métabolisme (suivi de la dynamique de flux métaboliques) et pour mieux raisonner les conduites de fermentation, notamment le régime de température. L'impact de ce régime de température est étudié plus en détail en réalisant des bilans de production lors de fermentations conduites avec la même souche de levure et le même moût. Les résultats obtenus montrent que si l'on ne prend en compte que les concentrations dans la phase liquide - généralement les seules informations disponibles - on surestime fortement l'impact de la température sur le métabolisme de composés fortement volatils, tels que les estersThe gas-liquid partitioning during winemaking fermentations was studied, with a focus on the main aroma compounds: isobutanol, isoamyl alcohol, ethyl acetate, isoamyl acetate, ethyl hexanoate. The partition coefficients (ki) of these molecules were quantified, using the PRV (Phase Ratio Variation) method. The influence of the must composition and temperature was assessed. The absence of any effect of CO2 production on gaz-liquid ratio compared to ki value was established by running specific fermentations in which the rate of CO2 production was kept constant by perfusion with assimilable nitrogen. A model was then elaborated to calculate gaz-liquid ratio of these molecules (excepted ethyl acetate and isoamyl alcohol) at any fermentation progress and at any temperature, including anisothermal fermentations. The model based on the equilibrium between the gas and liquid phases predicted ki with less than 10% error. Using this model, balances were c alculated, with a differentiation between (i) the total production, representative of the yeast potential (microbiological interest), (ii) the amount remaining in the fermenting must (organoleptic interest) and (iii) the amount lost in the exhaust CO2 (technological interest). High losses of esters were observed. For example, at 20°C, they represented 44% and 25 % for ethyl hexanoate and isoamyl acetate, respectively. Using an on line monitoring with a high frequency of measurements (one per hour), rates and specific rates of production were calculated. These new data are useful both for studies on metabolism (dynamics of metabolic fluxes) and for improving fermentation control, in particular temperature profile. The impact of temperature was assessed in more detail by comparing balances of production during fermentations run with the same yeast strain and must. These experiments demonstrated that the effect of temperature on the yeast metabolism was highly overestimated whe n only considering the concentrations in the liquid, i.e. usually the only available information
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Cantelli, Davide. "Utilizzo del Building Information Modeling (BIM) nella progettazione impiantistica: rebuilding punto vendita H&M Roma Da Vinci." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15503/.
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L’obiettivo della tesi è quello di evidenziare i vantaggi ottenuti dall’utilizzo della strategia BIM nella progettazione e realizzazione di un intervento edilizio. Si vuole altresì evidenziare la necessità di operare in maniera sinergica su un modello che riunisca tutte le discipline tecniche facenti parte del progetto, permettendo, tramite la digitalizzazione del processo progettuale, di ridurre le tempistiche e i costi, nonché di risolvere al meglio le problematiche e le interferenze tra i vari professionisti coinvolti che attualmente caratterizzano il processo edilizio.
Nella prima fase si affronta il tema del BIM, evidenziando l’evoluzione permessa da questo strumento di elaborazione e rappresentazione del progetto, sia dal punto di vista operativo e concettuale, sopratutto legata alla capacità di far cooperare al meglio le tre discipline principali che caratterizzano l’opera edilizia: l’aspetto architettonico, quello strutturale e quello impiantistico.
Analizzando il caso di studio si dimostra come la progettazione impiantistica possa essere facilitata e resa più efficiente grazie al supporto del processo BIM (Autodesk Revit 2017). Partendo dall’analisi degli ambienti e dei requisiti normativi, si realizza un modello tridimensionale del sistema impiantistico che offre non solo una pura rappresentazione geometrica, ma fornisce una esatta simulazione sul corretto funzionamento dello stesso, suggerendo eventuali migliorie possibili. Inoltre è possibile estrapolare dati tecnici e numerici riguardanti ogni componente o l’insieme dell’apparato impiantistico dell’edificio, rendendo possibili ulteriori simulazioni su rendimento, efficienza energetica e comfort. Si conclude che l’utilizzo del supporto BIM risulta fondamentale per migliorare il processo progettuale e produttivo, fornendo un dialogo efficiente e chiaro tra le varie discipline coinvolte, e uno strumento utilizzabile durante l’intera vita utile dell’edificio, dalla sua ideazione alla demolizione.
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Stitou, Adnane. "SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39785.
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This study aims to investigate short-to-medium forecasting and simulation of streamflow, water levels, and sediments in Canada using Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using python web interface was developed to generate short-term forecasts using SARIMA. The Akaike information criteria was used as performance criteria for generating efficient SARIMA models. The developed models were validated by analyzing the residuals. Several stations from the Canadian Hydrometric Database (HYDAT) displaying a linear upward or downward trend were identified to validate the methodology. Trends were detected using the Man-Kendall test.
The Nash-Sutcliffe efficiency coefficients (Nash ad Sutcliffe, 1970) of the developed models indicate that they are acceptable. The models can be used for short term (1 to 7 days) and medium-term (7 days to six months) forecasting of streamflow, water levels and sediments at all Canadian hydrometric stations. Such a forecast can be used for water resources management and help mitigate the effects of floods and droughts. The models can also be used to generate long time-series that can be used to test the performance of water resources systems.
Finally, we have automated the process of analysis, model-building and forecasting streamflow, water levels, and sediments by building a python-based application easily extendable and user-friendly. Therefore, automating the SARIMA calibration and forecasting process for all Canadian stations for the HYDAT database will prove to be a very useful tool for decision-makers and other entities in the field of hydrological study.
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Oleksandra, Shovkun. "Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34398.
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Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion.
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Ramos, Anthony Kojo. "Forecasting Mortality Rates using the Weighted Hyndman-Ullah Method." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54711.
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The performance of three methods of mortality modelling and forecasting are compared. These include the basic Lee–Carter and two functional demographic models; the basic Hyndman–Ullah and the weighted Hyndman–Ullah. Using age-specific data from the Human Mortality Database of two developed countries, France and the UK (England&Wales), these methods are compared; through within-sample forecasting for the years 1999-2018. The weighted Hyndman–Ullah method is adjudged superior among the three methods through a comparison of mean forecast errors and qualitative inspection per the dataset of the selected countries. The weighted HU method is then used to conduct a 32–year ahead forecast to the year 2050.
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Sklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.
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In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and so having error or distortion in between is almost a certainty. The channel introduces an acoustical mismatch between the speaker's signal and the trained data in the ASR, which results in poor recognition performance. The approach so far, has been to try to undo the havoc produced by the channels, i.e. compensate for the channel's behavior. In this thesis, we try to characterize the effects of different transmission media and use that as an inexpensive and repeatable way to train ASR systems.
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Sikorav, Jacques. "Sur l'identification et la modélisation de phénomènes non-stationnaires en acoustique : Equation des ondes dans les ouverts non-cylindriques." Paris 9, 1988. http://www.theses.fr/1988PA090029.
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Balembois, Stéphanie. "Ecrire en vain ? Le questionnement éthique dans Le jeu de patience, "archi-roman" de Louis Guilloux." Phd thesis, Université Paris-Est, 2008. http://tel.archives-ouvertes.fr/tel-00432050.
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Quel impératif pousse Guilloux à se tourner vers les autres? Provient-il d'un appel au sentiment ? D'un appel à la raison ? Est-il la réponse à une éthique ? Pourquoi cette nécessité de rendre la vie valable ? La valeur d'un homme se juge d'après ses actes semble dire Guilloux. Qu'est-ce qu'écrire en regard de l'action ? Comment écrire dans un monde en guerre ? L'écriture peut-elle être agissante ? Guilloux a voulu partager ses interrogations avec ses lecteurs, esquisser tous les cheminements possibles jusqu'à la contradiction. D'abord, montrer le processus de mythification entourant les actions des hommes qui ont précédé Guilloux dans cette vie. Ceux d'avant savaient agir ensemble et pour le bien de tous, ce savoir s'est perdu laissant l'individu seul face à ses doutes. Les tourments qui agitent le début du XXème siècle n'ont fait qu'exacerber le questionnement existentiel. Les divers degrés de responsabilité des hommes se dévoilent ainsi dans leurs manières de se conduire vis-à-vis d'autrui, autant d'engagements concrets ou de retraits qui attestent de l'humanité ou de l'inhumanité: " Trop et pas assez d'intelligence - trop et pas assez d'amour ". Ni la famille, ni la société, jaugée au travers de ses institutions, l'école, la justice, le clergé, ne répondent plus à l'exigence d'équité. Alors c'est aux hommes de s'unir. Agir, pour Guilloux, semble une forme de révolte et d'indignation contre la souffrance et l'injustice. Lutter, c'est aussi veiller sur la vie des autres. En refusant toutes compromissions, les personnages que Guilloux met en scène voient leur champ d'action se réduire à l'action humanitaire, quant à l'auteur lui revient le devoir de témoigner. Il se lance alors dans une écriture labyrinthique, un incessant jeu de miroir qui rapproche les hommes les plus différents : l'homme d'action, l'homme de lettres, l'homme militant, l'homme pas cru, l'homme en difficulté, l'homme perdu... chacun explore, estime, selon son itinéraire, la valeur accordée à sa vie et à celles des autres, hanté toujours par le devoir et la volonté de changer le monde
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Gherrabti, Abdelkrim. "Production de la 6-pentyl-(alpha)-pyrone à partir de culture de champignons filamenteux du genre Trichoderma : optimisation de sa biosynthèse en réacteur à lit fluidisé." Université Joseph Fourier (Grenoble ; 1971-2015), 1997. http://www.theses.fr/1997GRE10282.
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La biosynthese de la 6-pentyl, pyrone (arome : noix de coco) a partir l'espece de trichoderma a ete etudiee en fonction de differents parametres. La production de l'arome varie en fonction de la nature de la souche, de l'etat physiologique du champignon et du milieu de culture. Cette etude a ete realisee dans cinq milieux differents. Les milieux de culture, sabouraud avec 2 % de glucose et l'extrait de malt de berley, vis a vis d'une souche de trichoderma viride non sporulee, se revelent etre les plus performants tant pour la qualite que pour la quantite de pyrone produite. Une production maximale de 250 mg/l a ete obtenue en erlens de 250 ml. La biosynthese de la 6-pentyl, pyrone a ete ensuite mise en uvre dans un bioreacteur a lit fluidise de 2 litres. La cinetique de production de la pyrone a ete etablie et un rendement de 120 mg/l a ete obtenu en 192 heures. Dans le but de transferer nos resultats a une application industrielle, nous avons entrepris une etude chimiometrique de la biosynthese de la 6-pentyl, pyrone. Le modele previsionnel dans le domaine experimental etudie, a permis de determiner l'optimum de la production a 120-130 mg/l. , dans un bioreacteur de 2 litres.