[en] Time series databases are becoming omnipresent in several fields (e.g., computer sciences, finance, biology...), which makes the development of data mining algorithms for these objects a top priority among machine learning and statistics practitioners. This thesis deals with the construction of dif- ferent algorithms for time series data mining addressing a wide variety of problems. A novel dissimilarity measure between real-valued multivariate series defined in the frequency domain is proposed and used to construct several clustering algorithms whose main goal is to group the time series according to their underlying dependence structures. Both the crisp and the fuzzy clustering approaches are considered. The proposed spectral di- vergence measure is also combined with different statistical tools (including techniques of dimensionality reduction, wavelet decomposition, and func- tional data analysis) to introduce new and powerful algorithms to perform classification and outlier detection of multivariate time series. Clustering of times series with categorical response is also addressed by considering distances taking advantage of the specific properties of these data objects. The dissimilarity measures used for clustering purposes are also employed for testing the equality of the generating processes of two time series, which results in several hypothesis tests. A whole new different paradigm to per- form time series clustering is introduced by establishing the clustering pur- pose in terms of prediction accuracy. This realistic and challenging problem is addressed by means of a specific algorithm based on global forecasting methods. All the proposed techniques are properly formalized, their ad- vantages with respect to alternative methods are shown through extensive simulation experiments and their usefulness is illustrated by using real time series databases. Three software packages written in the R programming language are created to implement many of the constructed algorithms and their functionalities are explained by means of practical examples.

[gl] As bases de datos de series temporais están a volverse omnipresentes en varios campos (por exemplo, informática, finanzas, bioloxía...), o que fai que o desenvolvemento de algoritmos de minería de datos para estes obxectos sexa unha das principais prioridades para os profesionais da aprendizaxe automática e da estatística. Esta tese trata sobre a construción de diferentes algoritmos para a minería de datos de series temporais que abordan unha ampla variedade de problemas. Proponse unha nova medida de discrepancia entre series numéricas multivariantes definida no dominio da frecuencia que se utiliza para construír varios algoritmos de clustering cuxo principal obxectivo é agrupar as series temporais en base as súas estruturas de dependencia subxacentes. Considéranse os enfoques de clustering crisp e clustering fuzzy. Adicionalmente, a medida espectral de diverxencia proposta combínase con diferentes ferramentas estatísticas (por exemplo, técnicas de redución da dimensión, descomposición wavelet, e análise de datos funcionais) para introducir poderosos algoritmos de clasicación e de detección de anomalías nun contexto de series multivariantes. O clustering de series temporais con resposta categórica tamén se aborda mediante o uso de distancias que explotan as propiedades específicas deste tipo de series. As medidas de discrepancia utilizadas para clustering empréganse asímesmo para contrastar a igualdade dos procesos xeradores de dúas series de tempo, o que dá lugar a varios contrastes de hipóteses. Adicionalmente, introdúcese un paradigma completamente novo para abordar o problema de clustering de series temporais, que se basea en considerar a precisión das predicións como criterio para o agrupamento. Trátase dun problema realista e complexo, que se aborda mediante un algoritmo específi co baseado en modelos globais de predición. Todos os métodos propostos formalízanse adecuadamente, as súas vantaxes con respecto a enfoques alternativos corrobóranse mediante completos estudos de simulación e a súa utilidade ilústrase mediante o uso de bases de datos reais. Moitos dos algoritmos construídos impleméntanse nalgún dos tres paquetes que se desenvolven na linguaxe de programación R. As funcionalidades dos mesmos descríbense mediante varios exemplos.