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Integer-valued time series data appear in several diverse applications. However, modeling and inference for these types of dependent data pose several questions and interesting problems. The method of generalized linear models turns out to provide a sound framework for modeling and estimation whereby all computations are carried out by well-established software. I review this area of research and propose some other models. © 2009 John Wiley & Sons, Inc. 1 3 361 364

Information about the uncertainties associated with eddy covariance measurements of surface–atmosphere CO2 exchange is needed for data assimilation and inverse analyses to estimate model parameters, validation of ecosystem models against flux data, as well as multi-site synthesis activities (e.g., regional to continental integration) and policy decisionmaking. While model residuals (mismatch between fitted model predictions and measured fluxes) can potentially be analyzed to infer data uncertainties, the resulting uncertainty estimates may be sensitive to the particular model chosen. Here we use 10 site-years of data from the CarboEurope program, and compare the statistical properties of the inferred random flux measurement error calculated first using residuals from five different models, and secondly using paired observations made under similar environmental conditions. Spectral analysis of the model predictions indicated greater persistence (i.e., autocorrelation or ‘‘memory’’) compared to the measured values. Model residuals exhibited weaker temporal correlation, but were not uncorrelated white noise. Random flux measurement uncertainty, expressed as a standard deviation, was found to vary predictably in relation to the expected magnitude of the flux, in a manner that was nearly identical (for negative, but not positive, fluxes) to that reported previously for forested sites. Uncertainty estimates were generally comparable whether the uncertainty was inferred from model residuals or paired observations, although the latter approach resulted in somewhat smaller estimates. Higher order moments (e.g., skewness and kurtosis) suggested that for fluxes close to zero, the measurement error is commonly skewed and leptokurtic. Skewness could not be evaluated using the paired observation approach, because differencing of paired measurements resulted in a symmetric distribution of the inferred error. Patterns were robust and not especially sensitive to the model used, although more flexible models, which did not impose a particular functional form on relationships between environmental drivers and modeled fluxes, appeared to give the best results. We conclude that evaluation of flux measurement errors from model residuals is a viable alternative to the standard paired observation approach.

A new methodology for automated extraction of repeated patterns in time-series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed patterns in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chronological scanning of the time-series data. Thanks to this modelling global pattern filtering may be avoided and rich and highly pertinent results can be obtained. The modelling has been integrated in a collaborative pro ject between ethnomusicology, cognitive sciences and computer science, aimed at the study of Tunisian Modal Music., Une méthodologie d'extraction automatique de motifs répétés dans des séquences temporelles est présentée, dédiée en particulier à l'analyse de séquences musicales. L'approche initiale consiste en une recherche de motifs fermés dans un espace paramétrique multidimensionnel. Il est montré que ce premier mécanisme doit être articulé avec un système de découverte de motifs périodiques, ce qui implique un parcours strictement chronologique de la séquence. Cette modélisation permet d'éviter un filtrage global des patterns, et donc d'obtenir des résultats présentant une richesse et une pertinence élevée. La modélisation a été intégrée au sein d'un projet collaboratif entre ethnomusicologie, sciences cognitives et informatique, dédié à l'étude de la musique modale tunisienne.

Several studies have reported significant health effects of air pollution even at low levels of air pollutants, but in most of theses studies linear nonthreshold relations were assumed. We investigated the exposure–response association between ambient particles and mortality in the 22 European\ud cities participating in the APHEA (Air Pollution and Health—A European Approach) project, which is the largest available European database. We estimated the exposure–response curves using regression spline models with two knots and then combined the individual city estimates of the spline to get an overall exposure–response relationship. To further explore the heterogeneity in the observed city-specific exposure–response associations, we investigated several city descriptive variables as\ud potential effect modifiers that could alter the shape of the curve. We conclude that the association between ambient particles and mortality in the cities included in the present analysis, and in the range of the pollutant common in all analyzed cities, could be adequately estimated using the linear model. Our results confirm those previously reported in Europe and the United States. The heterogeneity\ud found in the different city-specific relations reflects real effect modification, which can be explained partly by factors characterizing the air pollution mix, climate, and the health of the population.

This paper discusses the use of Stata for the analysis of time series and panel data. The evolution of time-series capabilities in Stata is reviewed. Facilities for data management, graphics, and econometric analysis from both official Stata and the user community are discussed. A new routine to provide moving-window regression estimates—rollreg—is described, and its use illustrated.

The discovery of frequent sequential patterns in an ordered collection of data, such as sequential databases or time-series data, is an important issue in several contexts. In this paper, we employ formal concept analysis to develop the notion of closure for these sequential patterns and to characterize the concept lattice of the ordered contexts. The proposed concept lattice will serve as a model for the patterns extracted in the context of sequential databases by a recent algorithm (CloSpan, cite{Clospan}). Finally, we will show how we can also use our model to derive other kind of structured patterns, like the closed set of episodes in the context of time-series data cite{Toivonen}. So, the convenient transformation of the sequential patterns in the concepts of the lattice will give rise to the most representative set of parallel and serial closed episodes.

Author(s): Shwiff, Stephanie A.; Sterner, Ray T. | Abstract: Benefit-cost analysis (BCA) involves comparing all of the gains and losses from a given wildlife damage management action or management technique over time in similar units, thereby providing a total picture of potential gains and losses to society. This technique is at the core of justifications for wildlife damage management efforts. BCA has been noticeably absent from the study of vertebrate pest management problems, and in the few studies where a BCA has been included, the analysis is incomplete. This paper provides an overview of the steps in a BCA, using specific wildlife damage examples to highlight and expand the technique for researchers interested in documenting these effects.

To avoid the usual problems of multi-population correlation studies of air pollution and mortality, and for reasons of convenience, daily time-series mortality studies within single populations have recently become popular in air pollution epidemiology. Such studies describe how the short-term distribution of deaths relates to short-term fluctuations in air pollution levels. The regression-based risk coefficients from these acute-effects studies have been widely used to estimate the excess annual mortality within a population with a specified average level of air pollution. Such calculations are inappropriate. Since daily time-series data provide no simple direct information about the degree of life-shortening associated with the excess daily deaths (many of which are thought to be due to exacerbation of well-advanced disease, especially cardiovascular disease), such data cannot contribute to the estimation of the effects of air pollution upon chronic disease incidence and long-term death rates. Yet it is that category of effect that is of most public health importance.Such effects are best estimated from long-term cohort studies that incorporate good knowledge of local (or personal) exposure to air pollutants and of potential confounders. Time-series studies, properly evaluated, can identify the existence of acute toxic effects of transient peak levels of air pollution; they are thus useful for monitoring acute toxicity and for identifying the most noxious pollutants. However, to quantify the long-term health impacts of air pollution we cannot use acute-effects data.

Upper Ocean Processes Group meteorological data is transmitted from surface buoys via Argos satellite and processed in an automatic mode on a UNIX workstation. Data is extracted from input files based on instrument type and experiment, processed as appropriate, and plotted, without user intervention. While the processing system normally runs automatically, it is designed so that modules can also be run directly from a terminal when necessary. The Argos processing system allows us to monitor the meteorological data being collected in the field, and provides early information about problems with sensors, instruments, or buoys, when they occur. The automatic process allows more information to be viewed with less effort, and increases the usefulness of the Argos data.

[This corrects the article on p. 351 in vol. 9, PMID: 26483626.].

Significance Migratory activity is a critical factor in shaping processes of biological and cultural change through time. We introduce a method to estimate changes in underlying migratory activity that can be applied to genetic, morphological, or cultural data and is well-suited to samples that are sparsely distributed in space and through time. By applying this method to ancient genome data, we infer a number of changes in human mobility in Western Eurasia, including higher mobility in pre- than post-Last Glacial Maximum hunter–gatherers, and oscillations in Holocene mobility with peaks centering on the Neolithic transition and the beginnings of the Bronze Age and the Late Iron Age., Mobility is one of the most important processes shaping spatiotemporal patterns of variation in genetic, morphological, and cultural traits. However, current approaches for inferring past migration episodes in the fields of archaeology and population genetics lack either temporal resolution or formal quantification of the underlying mobility, are poorly suited to spatially and temporally sparsely sampled data, and permit only limited systematic comparison between different time periods or geographic regions. Here we present an estimator of past mobility that addresses these issues by explicitly linking trait differentiation in space and time. We demonstrate the efficacy of this estimator using spatiotemporally explicit simulations and apply it to a large set of ancient genomic data from Western Eurasia. We identify a sequence of changes in human mobility from the Late Pleistocene to the Iron Age. We find that mobility among European Holocene farmers was significantly higher than among European hunter–gatherers both pre- and postdating the Last Glacial Maximum. We also infer that this Holocene rise in mobility occurred in at least three distinct stages: the first centering on the well-known population expansion at the beginning of the Neolithic, and the second and third centering on the beginning of the Bronze Age and the late Iron Age, respectively. These findings suggest a strong link between technological change and human mobility in Holocene Western Eurasia and demonstrate the utility of this framework for exploring changes in mobility through space and time.

The management of renewable energy resources plays an important role in the availability, stability and energy quality in the modern power systems. A key role of the energy management policies is played by the primary resource availability adjustment to the required consumption. As the renewable energy resources are variable in time their forecast represents an important issue. In this paper is explored how the use of consecrated artificial intelligence techniques such as feed-forward and Elman neural networks are suitable for such energy production forecasting. The main reason is that artificial intelligence techniques offers a viable solution to correct the behavior of these systems while operating by learning the changes that occur as a result of power systems external and internal factors evolution. In this case the back-propagation learning algorithm was tested in different configurations of the neural networks to find an adequate solution for the specific datasets of solar photovoltaic renewable energy resource availability.

In general, dynamic systems are systems with time-dependent behavior. Dynamic systems are characterized by the non-stationary data sequences they emit. One particular way to model these non-stationary sequences is to consider them as a sequence of stationary segments, regimes, where each regime is separated by regime switching points from both the preceding and subsequent regimes. In system identification and monitoring applications, it is crucial to correctly and timely detect these regime switching points. One promising estimation method that may be used for detecting regime switching points is the stochastic learning based weak estimation (SLWE) method by Oommen and Rueda. We use SLWE for estimating First Order Markov (FOM) probabilities between symbols emitted by a system and for predicting regime switching points. A switching point is detected when the SLWE estimator unlearns, i.e., adapts estimates of FOM probabilities to new observations, such that the estimate re-converges to a new value that reflects, for the new regime, the FOM dependency of system output tokens. In experiments with a real Dataset for Human Activity Recognition, we see that our method has attractive efficiency (time and space) and similar accuracy compared with the state-of the-art. Experiments with synthetic data, where we controlled noise and Hamming distance between regimes, show promising accuracy for noise rates up to 25%, a rate at which accuracy of state-of-the-art methods deteriorates. Our method is flexible and can be configured to use not only FOM but also second-order and prior symbol probabilities, and combinations thereof.

Future increases in land-based production will need to focus more on sustainably intensifying existing production systems. Unfortunately, our understanding of the global patterns of land use intensity is weak, partly because land use intensity is a complex, multidimensional term, and partly because we lack appropriate datasets to assess land use intensity across broad geographic extents. Here, we review the state of the art regarding approaches for mapping land use intensity and provide a comprehensive overview of available global-scale datasets on land use intensity. We also outline major challenges and opportunities for mappinglanduseintensityfor cropland, grazing, and forestry systems, and identify key issues for future research.

【Background】 Atrial fibrillation (AF) is an arrhythmia commonly encountered in clinical practice. There is a high risk of thromboembolism in patients with AF. Nonlinear analyses such as electroencephalogram (EEG), electrocardiogram (ECG), and respiratory movement have been used to quantify biological signals, and sample entropy (SampEn) has been employed as a statistical measure to evaluate complex systems. In this study, we examined the values of SampEn in ECG signals for patients with and without AF to measure the regularity and complexity. 【Methods】 ECG signals of lead II were recorded from 34 subjects without arrhythmia and 15 patients with chronic AF in a supine position. The ECG signals were converted into time-series data and SampEn was calculated. 【Results】 The SampEn values for the group without arrhythmia were 0.252 ± 0.114 [time lag (τ) = 1] and 0.533 ± 0.163 (τ = 5), and those for the chronic AF group were 0.392 ± 0.158 (τ = 1) and 0.759 ± 0.246 (τ = 5). The values of SampEn were significantly higher in the group with chronic AF than in the group without arrhythmia (P < 0.01 for τ = 1, P < 0.004 for τ = 5). The constructed three-dimensional vectors were plotted in time-delayed three-dimensional space. We used time lags of τ = 5 and τ = 1. The shape of the loops of the three-dimensional space was better for τ = 5. 【Conclusion】 The values of SampEn from ECG for chronic AF patients were higher than for subjects without arrhythmia, suggesting greater complexity for the time-series from chronic AF patients. SampEn is considered a new index for evaluating complex systems in ECG.