Aplicación del método de series de Uranio al grafismo rupestre de estilo paleolítico: el caso de la cavidad de Covalanas (Ramales de la Victoria, Cantabria)

[EN] Se ensaya la aplicación del procedimiento analítico de series de Uranio para el conocimiento de la seriación artística de obras parietales de estilo paleolítico. El resultado obtenido muestra la validez del método y abre nuevas vías para la obtención de fechas absolutas "ante quem" y "post quem" que sirvan para enmarcar momentos de ejecución de obras rupestres.

Contributions to time series data mining departing from the problem of road travel time modeling

194 p.

Aperfeiçoamento da previsão global em séries temporais caóticas

A previsão de valores futuros em séries temporais produzidas por sistemas caóticos pode ser aplicada em diversas áreas do conhecimento como Astronomia, Economia, Física, Medicina, Meteorologia e Oceanografia. O método empregado consiste na reconstrução do espaço de fase seguido de um termo de melhoria da previsão. As rotinas utilizadas para a previsão e análise nesta linha de pesquisa fazem parte do pacote TimeS, que apresenta resultados encorajadores nas suas aplicações. O aperfeiçoamento das rotinas computacionais do pacote com vistas à melhoria da acurácia obtida e à redução do tempo computacional é construído a partir da investigação criteriosa da minimização empregada na obtenção do mapa global. As bases matemáticas são estabelecidas e novas rotinas computacionais são criadas. São ampliadas as possibilidades de funções de ajuste que podem incluir termos transcendentais nos componentes dos vetores reconstruídos e também possuir termos lineares ou não lineares nos parâmetros de ajuste. O ganho de eficiência atingido permite a realização de previsões e análises que respondem a perguntas importantes relacionadas ao método de previsão e ampliam a possibilidade de aplicações a séries reais.

Approximate Solutions by Truncated Taylor Series Expansions of Nonlinear Differential Equations and Related Shadowing Property with Applications

This paper investigates the errors of the solutions as well as the shadowing property of a class of nonlinear differential equations which possess unique solutions on a certain interval for any admissible initial condition. The class of differential equations is assumed to be approximated by well-posed truncated Taylor series expansions up to a certain order obtained about certain, in general nonperiodic, sampling points t(i) is an element of [t(0), t(J)] for i = 0, 1, . . . , J of the solution. Two examples are provided.

A 4500-year time series of Pacific cod (Gadus macrocephalus) size and abundance: archaeology, oceanic regime shifts, and sustainable fisheries

A 4500-year archaeological record of Pacific cod (Gadus macrocephalus) bones from Sanak Island, Alaska, was used to assess the sustainability of the modern fishery and the effects of this fishery on the size of fish caught. Allometric reconstructions of Pacific cod length for eight prehistoric time periods indicated that the current size of the nearshore, commercially fished Pacific cod stocks is statistically unchanged from that of fish caught during 4500 years of subsistence harvesting. This finding indicates that the current Pacific cod fishery that uses selective harvesting technolog ies is a sustainable commercial fishery. Variation in relative Pacific cod abundances provides further insights into the response of this species to punctuated changes in ocean climate (regime shifts) and indicates that Pacific cod stocks can recover from major environmental perturbations. Such palaeofisheries data can extend the short time-series of fisheries data (<50 yr) that form the basis for fisheries management in the Gulf of Alaska and place current trends within the context of centennial- or millennial-scale patterns.

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.