Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, and counties are nested within states. Multilevel models provide a statistical framework for investigating and drawing conclusions regarding the influence of factors at differing hierarchical levels of analysis. The work in this paper serves as an introduction to multilevel models and their comparison to Ordinary Least Squares (OLS) regression. We overview three basic model structures: variable intercept model, variable slope model, and hierarchical linear model and illustrate each model with an example of student data. Then, we contrast the three multilevel models with the OLS model and present a method for producing confidence intervals for the regression coefficients. / text

The objective of this research was to develop fluorescence-based tools that are suitable for performing rapid, accurate and direct characterization of natural organic matter (NOM) and colloidal/particulate substances present in natural water. Most available characterization methods are neither suitable for characterizing all the major NOM fractions such as protein-, humic acid-, fulvic acid- and polysaccharide-like substances as well as colloidal/particulate matter present in natural water nor are they suitable for rapid analyses. The individual and combined contributions of these NOM fractions and colloidal/particulate matter present in natural water contribute to membrane fouling, disinfection by-products formation and undesirable biological growth in drinking water treatment processes and distribution systems. The novel techniques developed in this research therefore, provide an avenue for improved understanding of these negative effects and proactive implementation of control and/or optimization strategies. The fluorescence excitation-emission matrix (EEM) method was used for characterization of NOM and colloidal/particulate matter present in water. Unlike most NOM and colloidal/particulate matter characterization techniques, this method can provide fast and consistent analyses with high instrumental sensitivity. The feasibility of using this method for monitoring NOM at very low concentration levels was also demonstrated with an emphasis on optimizing the instrument parameters necessary to obtain reproducible fluorescence signals. Partial least squares regression (PLS) was used to develop calibration models by correlating the fluorescence EEM intensities of water samples that contained surrogate NOM fractions with their corresponding dissolved organic carbon (DOC) concentrations. These fluorescence-based calibration models were found to be suitable for identifying/monitoring the extent of the relative changes that occur in different NOM fractions and the interactions between polysaccharide- and protein-like NOM in water treatment processes and distribution systems. Principal component analysis (PCA) of fluorescence EEMs was identified as a viable tool for monitoring the performance of biological filtration as a pre-treatment step, as well as ultrafiltration (UF) and nanofiltration (NF) membrane systems. The principal components (PCs) extracted in this approach were related to the major membrane foulant groups such as humic substances (HS), protein-like and colloidal/particulate matter in natural water. The PC score plots generated using the fluorescence EEMs obtained after just one hour of UF or NF operation could be related to high fouling events likely caused by elevated levels of colloidal/particulate-like material in the biofilter effluents. This fluorescence EEM-based PCA approach was sensitive enough to be used at low organic carbon levels present in NF permeate and has potential as an early detection method to identify high fouling events, allowing appropriate operational countermeasures to be taken. This fluorescence EEM-based PCA approach was also used to extract information relevant to reversible and irreversible membrane fouling behaviour in a bench-scale flat sheet cross flow UF process consisting of cycles of permeation and back-washing. PC score-based analysis revealed that colloidal/particulate matter mostly contributed to reversible fouling, while HS and protein-like matter were largely responsible for irreversible fouling. This method therefore has potential for monitoring modes of membrane fouling in drinking water treatment applications. The above approach was further improved by utilizing the evolution of the PC scores over the filtration time and relating these to membrane fouling by the use of PC scores??? balanced-based differential equations. Using these equations the proposed fluorescence-based modeling approach was capable of forecasting UF fouling behaviours with good accuracy based solely on fluorescence data obtained at time = 15 min from the initiation of the filtration process. In addition, this approach was tested experimentally as a basis for optimization by modifying the UF back-washing times with the objective of minimizing energy consumption and maximizing water production. Preliminary optimization results demonstrated the potential of this approach to reduce power consumption by significant percentages. This approach was also useful for identifying the fouling components of the NOM that were contributing to reversible and irreversible membrane fouling. Grand River water (Southwestern Ontario, Canada) was used as the natural water source for developing the techniques presented in this thesis. Future research focusing on testing these methods for monitoring of membrane fouling and treatment processes in large-scale drinking water treatment facilities that experience different sources of raw water would be useful for identifying the limitation of these techniques and areas for improvements.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2018-08-01T12:06:20Z No. of bitstreams: 2 Dissertação - Theófilo Machado de Sousa Neto - 2018.pdf: 5352330 bytes, checksum: 633a1463e2e997810ceffbed30fe9665 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-08-01T13:38:17Z (GMT) No. of bitstreams: 2 Dissertação - Theófilo Machado de Sousa Neto - 2018.pdf: 5352330 bytes, checksum: 633a1463e2e997810ceffbed30fe9665 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-08-01T13:38:17Z (GMT). No. of bitstreams: 2 Dissertação - Theófilo Machado de Sousa Neto - 2018.pdf: 5352330 bytes, checksum: 633a1463e2e997810ceffbed30fe9665 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-06-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Given the need to discuss mathematical methods capable of adjusting curves that represent experimental data. This work presents seven methods of curves adjustment, three of these, methods that use least squares regression techniques and the other four, using interpolation techniques. Initially, it brings some definitions that present to the reader all the mathematical foundation that rules the equations. In parallel, it seeks to discuss, through examples, the area of attribution of the described methods, realizing whenever possible a comparation between the several techniques presented and their errors in the estimates. In order to demonstrate that the techniques discussed here are feasible for use in basic education, it exposes an experience of applying one of these methods in solving a basic problem of the discipline of Physics. After presenting the step-by-step method of obtaining soil resistivity, a variable that is of the utmost importance for the elaboration of projects for grounding meshes that supply energy substations, We finish this work by solving the problem with the aid of adjustment techniques curves studied, proposing the inclusion of the methods addressed in one of the steps of the procedure to obtain soil resistivity. / Diante da necessidade de se discutir sobre métodos matemáticos capazes de ajustar curvas que representem dados experimentais. Este trabalho apresenta como escopo sete métodos de ajustes de curvas, sendo que três destes, utilizam as técnicas de regressão por mínimos quadrados e os outros quatro, usando técnicas de interpolação. Inicialmente, traremos algumas definições que apresentam ao leitor todo o embasamento matemático que rege os equacionamentos. Em paralelo, procuramos discutir, através de exemplos, a área de atribuição dos métodos descritos, realizando sempre que possível um comparativo entre as variadas técnicas apresentadas e seus erros nas estimativas.Com o intuito de demonstrar que as técnicas aqui discutidas são viáveis para utilização na educação básica, apresentaremos uma experiência de aplicação de um desses métodos na resolução de um problema básico da disciplina de Física. Após relatar os procedimentos do método de obtenção da resistividade do solo, que é uma variável de suma importância para a elaboração de projetos de malhas de aterramento que atendem subestações de energia. Finaliza-se este trabalho resolvendo o problema com auxílio das técnicas de ajustes de curva estudados, propondo a inclusão dos métodos abordados em uma das etapas do procedimento de obtenção da resistividade do solo.

Made available in DSpace on 2016-08-19T12:56:41Z (GMT). No. of bitstreams: 1 Dissertacao Marcelo Jose.pdf: 1590491 bytes, checksum: 7be3e83649dd910e0afe9a5a25de4e73 (MD5) Previous issue date: 2013-10-22 / The ethanol has continuously gained interests in many countries including Brazil due to the PROÁLCOOL program. The experimental determination of properties of ethanol and other fuels through official methods is very time consuming as well as tedious process. The estimation of these properties with the help of computational tools can be very useful. In the present work, the methods of partial least squares regression (PLS) and artificial neural network multilayer (ANN) were used to estimate one of the most important properties of fuel ethanol, density, using official quality parameters for ethanol, collected from LAPQAP/UFMA laboratory corresponding to 12 years (period: 2002-2013) of analyzes. A careful analysis of the data was performed to obtain a set of variables and data that best represents satisfactory performance of the two models. The estimates of both approaches were compared and validated. The predictive ability of the network obtained was very good for the parameters studied, consistent with the accuracy of the experimental measurements. The low mean square error, the randomness, the zero mean and the constant variance, obtained for the residues, indicated the suitability of the models, suggesting their use to estimate (predict) the density of ethanol. Results indicated that the model ANN was adequate, and the value of NMSE (normalized mean square error) of 0.0012, less than the PLS model of 0.2221. The result achieved is less than the range of measurement uncertainty of the equipment responsible for testing the density proving that the model used has satisfactory performance. / O etanol tem alcançado crescente interesse em muitos países, principalmente, no Brasil devido ao programa PROÁLCOOL. A determinação experimental das propriedades deste biocombustível e de outros combustíveis por meio de métodos oficiais é muito demorada, bem como é considerado um tedioso processo. A estimativa dessas propriedades com a ajuda de ferramentas computacionais pode ser de grande utilidade. No presente trabalho, os métodos de regressão por mínimos quadrados parciais (PLS) e redes neurais artificiais de múltiplas camadas (RNA) foram usados para estimar uma das mais importantes propriedades do etanol combustível, massa específica, utilizando parâmetros de qualidade oficiais de etanol, oriundos de análises realizadas no laboratório LAPQAP/UFMA, durante 12 anos (período: 2002-2013). Inicialmente, uma análise cuidadosa dos dados foi realizada a fim de selecionar um conjunto de variáveis e dados que melhor representasse um desempenho satisfatório dos dois modelos estudados. As estimativas de ambas as abordagens foram comparadas e validadas. A capacidade preditiva da rede neural obtida foi considerada muito boa para os parâmetros estudados, e compatível com a precisão das medidas experimentais. O baixo erro quadrático médio, a aleatoriedade, a média nula e a variância constante, obtida para os resíduos, evidenciaram a adequabilidade dos modelos usados, sugerindo a utilização destes modelos para estimar (predizer) a massa específica do etanol. Resultados indicaram que o modelo de RNA foi adequado, sendo o valor de NMSE (erro quadrático médio normalizado) de 0,0012, valor este, muito inferior ao modelo de PLS de 0,2221. Este resultado alcançado é inferior aos valores da faixa de incerteza de medição do equipamento responsável pelo ensaio experimental da massa específica, comprovando que o modelo utilizado possui desempenho considerado muito bom.

Due to the invasive nature of quantification techniques, baseline pigment data for coral-dwelling zooxanthellae are not known. In an attempt to develop a model for non-invasive estimation of zooxanthellae pigment concentrations from corals, field samples were taken from Porites rus and P. lutea in Apra Harbor, Guam. In-situ reflectance spectra (R400-R800) from 22 coral colonies were collected. “Coral truthing” was accomplished by extracting corresponding tissue core samples. Subsequent analysis to quantify the concentrations of 6 zooxanthellae pigments (µg cm-2) was performed using HPLC. Trials of multiple linear regressions were attempted (EJ Hochberg) and found inappropriate, despite previous success. The multivariate calibration technique partial least squares regression (PLS-R) is an excellent tool in the case of co-linear variables. Thus, PLS-R was attempted for chlorophyll c2 and peridinin after demonstration of co-linearity. This may be an appropriate approach for development of bio-optical models to estimate zooxanthellae pigment concentrations. Further, the dinoflagellate diagnostic pigment peridinin may be of great value for reef-scale remote sensing of changes in coral status in the future.

Background: Joint stability has been experimentally and clinically linked to mechanisms of knee injury and joint degeneration. The only dynamic, and perhaps most important, regulators of knee joint stability are contributions from muscular contractions. In participants with unstable knees, such as anterior cruciate ligament (ACL) injured, a range of neuromuscular adaptations has been observed including quadriceps weakness and increased co-activation of adjacent musculature. This co-activation is seen as a compensation strategy to increase joint stability. In fact, despite increased co-activation, instability persists and it remains unknown whether observed adaptations are the result of injury induced quadriceps weakness or the mechanical instability itself. Furthermore, there exists conflicting evidence on how and which of the neuromuscular adaptations actually improve and/or reduce knee joint stability. Purpose: The overall aim of this thesis is therefore to elucidate the role of injury and muscle weakness on muscular contributions to knee joint stability by addressing two main objectives: (1) to further our understanding of individual muscle contribution to internal knee joint moments; and (2) to investigate neuromuscular adaptations, and their effects on knee joint moments, caused by either ACL injury and experimental voluntary quadriceps inhibition (induced by pain). Methods: The relationship between individual muscle activation and internal net joint moments was quantified using partial least squares regression models. To limit the biomechanical contributions to force production, surface electromyography (EMG) and kinetic data was elicited during a weight-bearing isometric force matching task. A cross-sectional study design determined differences in individual EMG-moment relationships between ACL deficient and healthy controls (CON) groups. A crossover placebo controlled study design determined these differences in healthy participants with and without induced quadriceps muscle pain. Injections of hypertonic saline (5.8%) to the vastus medialis induced muscle pain. Isotonic saline (0.9%) acted as control. Effect of muscle pain on muscle synergies recruited for the force matching task, lunging and squatting tasks was also evaluated. Synergies were extracted using a concatenated non-negative matrix factorization framework. Results/Discussion: In CON, significant relationships of the rectus femoris and tensor fascia latae to knee extension and hip flexion; hamstrings to hip extension and knee flexion; and gastrocnemius and hamstrings to knee rotation were identified. Vastii activation was independent of moment generation, suggesting mono-articular vastii activate to produce compressive forces, essentially bracing the knee, so that bi-articular muscles crossing the hip can generate moments for the purpose of sagittal plane movement. Hip ab/adductor muscles modulate frontal plane moments, while hamstrings and gastrocnemius support the knee against externally applied rotational moments. Compared to CON, ACL had 1) stronger relationships between rectus femoris and knee extension, semitendinosus and knee flexion, and gastrocnemius and knee flexion moments; and 2) weaker relationships between biceps femoris and knee flexion, gastrocnemius and external knee rotation, and gluteus medius and hip abduction moments. Since the knee injury mechanism, is associated with shallow knee flexion angles, valgus alignment and rotation, adaptations after ACL injury are suggested to improve sagittal plane stability, but reduce frontal and rotational plane stability. During muscle pain, EMG-moment relationships of 1) semitendinosus and knee flexor moments were stronger compared to no pain, while 2) rectus femoris and tensor fascia latae to knee extension moments and 3) semitendinosus and lateral gastrocnemius to knee internal rotation moments were reduced. Results support the theory that adaptations to quadriceps pain reduces knee extensor demand to protect the joint and prevent further pain; however, changes in non-painful muscles reduce rotational plane stability. Individual muscle synergies were identified for each moment type: flexion and extension moments were respectively accompanied by dominant hamstring and quadriceps muscle synergies while co-activation was observed in muscle synergies associated with abduction and rotational moments. Effect of muscle pain was not evident on muscle synergies recruited for the force matching task. This may be due to low loading demands and/or a subject-specific redistribution of muscle activation. Similarly, muscle pain did not affect synergy composition in lunging and squatting tasks. Rather, activation of the extensor dominant muscle synergy and knee joint dynamics were reduced, supporting the notion that adaptive response to pain is to reduce the load and risk of further pain and/or injury. Conclusion: This thesis evaluated the interrelationship between muscle activation and internal joint moments and the effect of ACL injury and muscle pain on this relationship. Findings indicate muscle activation is not always dependent on its anatomical orientation as previous works suggest, but rather on its role in maintaining knee joint stability especially in the frontal and transverse loading planes. In tasks that are dominated by sagittal plane loads, hamstring and quadriceps will differentially activate. However, when the knee is required to resist externally applied rotational and abduction loads, strategies of global co-activation were identified. Contributions from muscles crossing the knee for supporting against knee adduction loads were not apparent. Alternatively hip abductors were deemed more important regulators of knee abduction loads. Both muscle pain and ACL groups demonstrated changes in muscle activation that reduced rotational stability. Since frontal plane EMG-moment changes were not present during muscle pain, reduced relationships between hip muscles and abduction moments may be chronic adaptions by ACL that facilitate instability. Findings provide valuable insight into the roles muscles play in maintaining knee joint stability. Rehabilitative/ preventative exercise interventions should focus on neuromuscular training during tasks that elicit rotational and frontal loads (i.e. side cuts, pivoting maneuvers) as well as maintaining hamstring balance, hip abductor and plantarflexor muscle strength in populations with knee pathologies and quadriceps muscle weakness.

[ES] De acuerdo con las regulaciones europeas y muchos estudios científicos, es necesario monitorear y analizar las condiciones microclimáticas en museos o edificios, para preservar las obras de arte en ellos. Con el objetivo de ofrecer herramientas para el monitoreo de las condiciones climáticas en este tipo de edificios, en esta tesis doctoral se propone una nueva metodología estadística para clasificar series temporales de parámetros climáticos como la temperatura y humedad relativa. La metodología consiste en aplicar un método de clasificación usando variables que se computan a partir de las series de tiempos. Los dos primeros métodos de clasificación son versiones conocidas de métodos sparse PLS que no se habían aplicado a datos correlacionados en el tiempo. El tercer método es una nueva propuesta que usa dos algoritmos conocidos. Los métodos de clasificación se basan en diferentes versiones de un método sparse de análisis discriminante de mínimos cuadra- dos parciales PLS (sPLS-DA, SPLSDA y sPLS) y análisis discriminante lineal (LDA). Las variables que los métodos de clasificación usan como input, corresponden a parámetros estimados a partir de distintos modelos, métodos y funciones del área de las series de tiempo, por ejemplo, modelo ARIMA estacional, modelo ARIMA- TGARCH estacional, método estacional Holt-Winters, función de densidad espectral, función de autocorrelación (ACF), función de autocorrelación parcial (PACF), rango móvil (MR), entre otras funciones. También fueron utilizadas algunas variables que se utilizan en el campo de la astronomía para clasificar estrellas. En los casos que a priori no hubo información de los clusters de las series de tiempos, las dos primeras componentes de un análisis de componentes principales (PCA) fueron utilizadas por el algoritmo k- means para identificar posibles clusters de las series de tiempo. Adicionalmente, los resultados del método sPLS-DA fueron comparados con los del algoritmo random forest. Tres bases de datos de series de tiempos de humedad relativa o de temperatura fueron analizadas. Los clusters de las series de tiempos se analizaron de acuerdo a diferentes zonas o diferentes niveles de alturas donde fueron instalados sensores para el monitoreo de las condiciones climáticas en los 3 edificios.El algoritmo random forest y las diferentes versiones del método sparse PLS fueron útiles para identificar las variables más importantes en la clasificación de las series de tiempos. Los resultados de sPLS-DA y random forest fueron muy similares cuando se usaron como variables de entrada las calculadas a partir del método Holt-Winters o a partir de funciones aplicadas a las series de tiempo. Aunque los resultados del método random forest fueron levemente mejores que los encontrados por sPLS-DA en cuanto a las tasas de error de clasificación, los resultados de sPLS- DA fueron más fáciles de interpretar. Cuando las diferentes versiones del método sparse PLS utilizaron variables resultantes del método Holt-Winters, los clusters de las series de tiempo fueron mejor discriminados. Entre las diferentes versiones del método sparse PLS, la versión sPLS con LDA obtuvo la mejor discriminación de las series de tiempo, con un menor valor de la tasa de error de clasificación, y utilizando el menor o segundo menor número de variables.En esta tesis doctoral se propone usar una versión sparse de PLS (sPLS-DA, o sPLS con LDA) con variables calculadas a partir de series de tiempo para la clasificación de éstas. Al aplicar la metodología a las distintas bases de datos estudiadas, se encontraron modelos parsimoniosos, con pocas variables, y se obtuvo una discriminación satisfactoria de los diferentes clusters de las series de tiempo con fácil interpretación. La metodología propuesta puede ser útil para caracterizar las distintas zonas o alturas en museos o edificios históricos de acuerdo con sus condiciones climáticas, con el objetivo de prevenir problemas de conservación con las obras de arte. / [CA] D'acord amb les regulacions europees i molts estudis científics, és necessari monitorar i analitzar les condiciones microclimàtiques en museus i en edificis similars, per a preservar les obres d'art que s'exposen en ells. Amb l'objectiu d'oferir eines per al monitoratge de les condicions climàtiques en aquesta mena d'edificis, en aquesta tesi es proposa una nova metodologia estadística per a classificar series temporals de paràmetres climàtics com la temperatura i humitat relativa.La metodologia consisteix a aplicar un mètode de classificació usant variables que es computen a partir de les sèries de temps. Els dos primers mètodes de classificació són versions conegudes de mètodes sparse PLS que no s'havien aplicat adades correlacionades en el temps. El tercer mètode és una nova proposta que usados algorismes coneguts. Els mètodes de classificació es basen en diferents versions d'un mètode sparse d'anàlisi discriminant de mínims quadrats parcials PLS (sPLS-DA, SPLSDA i sPLS) i anàlisi discriminant lineal (LDA). Les variables queels mètodes de classificació usen com a input, corresponen a paràmetres estimats a partir de diferents models, mètodes i funcions de l'àrea de les sèries de temps, per exemple, model ARIMA estacional, model ARIMA-TGARCH estacional, mètode estacional Holt-Winters, funció de densitat espectral, funció d'autocorrelació (ACF), funció d'autocorrelació parcial (PACF), rang mòbil (MR), entre altres funcions. També van ser utilitzades algunes variables que s'utilitzen en el camp de l'astronomia per a classificar estreles. En els casos que a priori no va haver-hi información dels clústers de les sèries de temps, les dues primeres components d'una anàlisi de components principals (PCA) van ser utilitzades per l'algorisme k-means per a identificar possibles clústers de les sèries de temps. Addicionalment, els resultats del mètode sPLS-DA van ser comparats amb els de l'algorisme random forest.Tres bases de dades de sèries de temps d'humitat relativa o de temperatura varen ser analitzades. Els clústers de les sèries de temps es van analitzar d'acord a diferents zones o diferents nivells d'altures on van ser instal·lats sensors per al monitoratge de les condicions climàtiques en els edificis.L'algorisme random forest i les diferents versions del mètode sparse PLS van ser útils per a identificar les variables més importants en la classificació de les series de temps. Els resultats de sPLS-DA i random forest van ser molt similars quan es van usar com a variables d'entrada les calculades a partir del mètode Holt-winters o a partir de funcions aplicades a les sèries de temps. Encara que els resultats del mètode random forest van ser lleument millors que els trobats per sPLS-DA quant a les taxes d'error de classificació, els resultats de sPLS-DA van ser més fàcils d'interpretar.Quan les diferents versions del mètode sparse PLS van utilitzar variables resultants del mètode Holt-Winters, els clústers de les sèries de temps van ser més ben discriminats. Entre les diferents versions del mètode sparse PLS, la versió sPLS amb LDA va obtindre la millor discriminació de les sèries de temps, amb un menor valor de la taxa d'error de classificació, i utilitzant el menor o segon menor nombre de variables.En aquesta tesi proposem usar una versió sparse de PLS (sPLS-DA, o sPLS amb LDA) amb variables calculades a partir de sèries de temps per a classificar series de temps. En aplicar la metodologia a les diferents bases de dades estudiades, es van trobar models parsimoniosos, amb poques variables, i varem obtindre una discriminació satisfactòria dels diferents clústers de les sèries de temps amb fácil interpretació. La metodologia proposada pot ser útil per a caracteritzar les diferents zones o altures en museus o edificis similars d'acord amb les seues condicions climàtiques, amb l'objectiu de previndre problemes amb les obres d'art. / [EN] According to different European Standards and several studies, it is necessary to monitor and analyze the microclimatic conditions in museums and similar buildings, with the goal of preserving artworks. With the aim of offering tools to monitor the climatic conditions, a new statistical methodology for classifying time series of different climatic parameters, such as relative humidity and temperature, is pro- posed in this dissertation.The methodology consists of applying a classification method using variables that are computed from time series. The two first classification methods are ver- sions of known sparse methods which have not been applied to time dependent data. The third method is a new proposal that uses two known algorithms. These classification methods are based on different versions of sparse partial least squares discriminant analysis PLS (sPLS-DA, SPLSDA, and sPLS) and Linear Discriminant Analysis (LDA). The variables that are computed from time series, correspond to parameter estimates from functions, methods, or models commonly found in the area of time series, e.g., seasonal ARIMA model, seasonal ARIMA-TGARCH model, seasonal Holt-Winters method, spectral density function, autocorrelation function (ACF), partial autocorrelation function (PACF), moving range (MR), among others functions. Also, some variables employed in the field of astronomy (for classifying stars) were proposed.The methodology proposed consists of two parts. Firstly, different variables are computed applying the methods, models or functions mentioned above, to time series. Next, once the variables are calculated, they are used as input for a classification method like sPLS-DA, SPLSDA, or SPLS with LDA (new proposal). When there was no information about the clusters of the different time series, the first two components from principal component analysis (PCA) were used as input for k-means method for identifying possible clusters of time series. In addition, results from random forest algorithm were compared with results from sPLS-DA.This study analyzed three sets of time series of relative humidity or temperate, recorded in different buildings (Valencia's Cathedral, the archaeological site of L'Almoina, and the baroque church of Saint Thomas and Saint Philip Neri) in Valencia, Spain. The clusters of the time series were analyzed according to different zones or different levels of the sensor heights, for monitoring the climatic conditions in these buildings.Random forest algorithm and different versions of sparse PLS helped identifying the main variables for classifying the time series. When comparing the results from sPLS-DA and random forest, they were very similar for variables from seasonal Holt-Winters method and functions which were applied to the time series. The results from sPLS-DA were easier to interpret than results from random forest. When the different versions of sparse PLS used variables from seasonal Holt- Winters method as input, the clusters of the time series were identified effectively.The variables from seasonal Holt-Winters helped to obtain the best, or the second best results, according to the classification error rate. Among the different versions of sparse PLS proposed, sPLS with LDA helped to classify time series using a fewer number of variables with the lowest classification error rate.We propose using a version of sparse PLS (sPLS-DA, or sPLS with LDA) with variables computed from time series for classifying time series. For the different data sets studied, the methodology helped to produce parsimonious models with few variables, it achieved satisfactory discrimination of the different clusters of the time series which are easily interpreted. This methodology can be useful for characterizing and monitoring micro-climatic conditions in museums, or similar buildings, for preventing problems with artwork. / I gratefully acknowledge the financial support of Pontificia Universidad Javeriana Cali – PUJ and Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior – ICETEX who awarded me the scholarships ’Convenio de Capacitación para Docentes O. J. 086/17’ and ’Programa Crédito Pasaporte a la Ciencia ID 3595089 foco-reto salud’ respectively. The scholarships were essential for obtaining the Ph.D. Also, I gratefully acknowledge the financial support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 814624. / Ramírez Buelvas, SM. (2022). A Statistical Methodology for Classifying Time Series in the Context of Climatic Data [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181123 / TESIS

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24041号 / 情博第797号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 下平 英寿, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM