Representing the Arctic in global surface temperature time series of recent climate change

The Arctic is an important region in the study of climate change, but monitoring surface temperatures in this region is challenging, particularly in areas covered by sea ice. Here in situ, satellite and reanalysis data were utilised to investigate whether global warming over recent decades could be better estimated by changing the way the Arctic is treated in calculating global mean temperature. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques. Kriging techniques provided the smallest errors in anomaly estimates. Similar accuracies were found for anomalies estimated from in situ meteorological station SAT records using a kriging technique. Whether additional data sources, which are not currently utilised in temperature anomaly datasets, would improve estimates of Arctic surface air temperature anomalies was investigated within the reanalysis testbed and using in situ data. For the reanalysis study, the additional input anomalies were reanalysis data sampled at certain supplementary data source locations over Arctic land and sea ice areas. For the in situ data study, the additional input anomalies over sea ice were surface temperature anomalies derived from the Advanced Very High Resolution Radiometer satellite instruments. The use of additional data sources, particularly those located in the Arctic Ocean over sea ice or on islands in sparsely observed regions, can lead to substantial improvements in the accuracy of estimated anomalies. Decreases in Root Mean Square Error can be up to 0.2K for Arctic-average anomalies and more than 1K for spatially resolved anomalies. Further improvements in accuracy may be accomplished through the use of other data sources.

Stabilizing Nonstationary Electrochemical Time Series

Electrochemical systems are ideal working-horses for studying oscillatory dynamics. Experimentally obtained time series, however, are usually associated with a spontaneous drift in some uncontrollable parameter that triggers transitions among different oscillatory patterns, despite the fact that all controllable parameters are kept constant. Herein we present an empirical method to stabilize experimental potential time series. The method consists of applying a negative galvanodynamic sweep to compensate the spontaneous drift and was tested for the oscillatory electro-oxidation of methanol on platinum. For a wide range of applied currents, the base system presents spontaneous transitions from quasi-harmonic to mixed mode oscillations. Temporal patterns were stabilized by galvanodynamic sweeps at different rates. The procedure resulted in a considerable increase in the number of oscillatory cycles from 5 to 20 times, depending on the specific temporal pattern. The spontaneous drift has been associated with uncompensated oscillations, in which the coverage of some adsorbed species are not reestablished after one cycle; i.e., there is a net accumulation and/or depletion of adsorbed species during oscillations. We interpreted the rate of the galvanodynamic sweep in terms of the time scales of the poisoning processes that underlies the uncompensated oscillations and thus the spontaneous slow drift.

Public debt sustainability and endogenous seignorage in Brazil: time-series evidence from 1947-92

Using national accounts data for the revenue-GDP and expenditure GDP ratios from 1947 to 1992, we examine two central issues in public finance. First, was the path of public debt sustainable during this period? Second, if debt is sustainable, how has the government historically balanced the budget after hocks to either revenues or expenditures? The results show that (i) public deficit is stationary (bounded asymptotic variance), with the budget in Brazil being balanced almost entirely through changes in taxes, regardless of the cause of the initial imbalance. Expenditures are weakly exogenous, but tax revenues are not;(ii) a rational Brazilian consumer can have a behavior consistent with Ricardian Equivalence (iii) seignorage revenues are critical to restore intertemporal budget equilibrium, since, when we exclude them from total revenues, debt is not sustainable in econometric tests.

Output convergence in Mercosur: multivariate time series evidence

The aim of this paper is to provide evidence on output convergence among the Mercosur countries and associates, using multivariate time-series tests. The methodology is based on a combination of tests and estimation procedures, both univariate and multivariate, applied to the differences in per capita real income. We use the definitions of time-series convergence proposed by Bernard & Durlauf and apply unit root and tests proposed by Abuaf & Jorion and Taylor & Sarno. In this same multivariate context, the Flôres, Preumont & Szafarz and Breuer, MbNown & Wallace tests, which allow for the existence of correlations across the series without imposing a common speed of mean reversion, identify the countries that convergence. Concerning the empirical results, there is evidence of long-run convergence or, at least, catching up, for the smaller countries, Bolivia, Paraguay, Peru and Uruguay, towards Brazil and, to some extent, Argentina. In contrast, the evidence on convergence for the larger countries is weaker, as they have followed different (or rather opposing) macroeconomic policy strategies. Thus the future of the whole area will critically depend on the ability of Brazil, Argentina and Chile to find some scope for more cooperative policy actions.

Do shocks permanently change output? : Local persistency in economic time series

While it is recognized that output fuctuations are highly persistent over certain range, less persistent results are also found around very long horizons (Conchrane, 1988), indicating the existence of local or temporary persistency. In this paper, we study time series with local persistency. A test for stationarity against locally persistent alternative is proposed. Asymptotic distributions of the test statistic are provided under both the null and the alternative hypothesis of local persistency. Monte Carlo experiment is conducted to study the power and size of the test. An empirical application reveals that many US real economic variables may exhibit local persistency.

Public debt sustainability and endogenous seigniorage in Brazil: time-series evidence from 1947-92: revised version

Using national accounts data for the revenue-GDP and expenditureGDP ratios from 1947 to 1992, we examine three central issues in public finance. First, was the path of public debt sustainable during this period? Second, if debt is sustainable, how has the government historically balanced the budget after shocks to either revenues or expenditures? Third, are expenditures exogenous? The results show that (i) public deficit is stationary (bounded asymptotic variance), with the budget in Brazil being balanced almost entirely through changes in taxes, regardless of the cause of the initial imbalance. Expenditures are weakly exogenous, but tax revenues are not; (ii) the behavior of a rational Brazilian consumer may be consistent with Ricardian Equivalence; (iii) seigniorage revenues are critical to restore intertemporal budget equilibrium, since, when we exclude them from total revenues, debt is not sustainable in econometric tests.

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Time series analysis of jaw muscle contraction and tissue deformation during mastication in miniature pigs

Masticatory muscle contraction causes both jaw movement and tissue deformation during function. Natural chewing data from 25 adult miniature pigs were studied by means of time series analysis. The data set included simultaneous recordings of electromyography (EMG) from bilateral masseter (MA), zygomaticomandibularis (ZM) and lateral pterygoid muscles, bone surface strains from the left squamosal bone (SQ), condylar neck (CD) and mandibular corpus (MD), and linear deformation of the capsule of the jaw joint measured bilaterally using differential variable reluctance transducers. Pairwise comparisons were examined by calculating the cross-correlation functions. Jaw-adductor muscle activity of MA and ZM was found to be highly cross-correlated with CD and SQ strains and weakly with MD strain. No muscle’s activity was strongly linked to capsular deformation of the jaw joint, nor were bone strains and capsular deformation tightly linked. Homologous muscle pairs showed the greatest synchronization of signals, but the signals themselves were not significantly more correlated than those of non-homologous muscle pairs. These results suggested that bone strains and capsular deformation are driven by different mechanical regimes. Muscle contraction and ensuing reaction forces are probably responsible for bone strains, whereas capsular deformation is more likely a product of movement.

Comparing non-stationary and irregularly spaced time series

In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodograms considering stationary and non-stationary time series generated from independent Gaussian processes. We also adapt an existing procedure to use this statistic and its approximate distribution in order to test if two regularly or irregularly spaced time series are realizations of the same generating process. Simulation studies show good size and power properties for the test statistic. An application with financial microdata illustrates the test usefulness. We conclude advocating the use of these approximate distributions instead of the ones obtained through randomizations, mainly in the case of irregular time series. (C) 2012 Elsevier B.V. All rights reserved.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Efficient subsequence alignment of time series

Zeitreihen sind allgegenwärtig. Die Erfassung und Verarbeitung kontinuierlich gemessener Daten ist in allen Bereichen der Naturwissenschaften, Medizin und Finanzwelt vertreten. Das enorme Anwachsen aufgezeichneter Datenmengen, sei es durch automatisierte Monitoring-Systeme oder integrierte Sensoren, bedarf außerordentlich schneller Algorithmen in Theorie und Praxis. Infolgedessen beschäftigt sich diese Arbeit mit der effizienten Berechnung von Teilsequenzalignments. Komplexe Algorithmen wie z.B. Anomaliedetektion, Motivfabfrage oder die unüberwachte Extraktion von prototypischen Bausteinen in Zeitreihen machen exzessiven Gebrauch von diesen Alignments. Darin begründet sich der Bedarf nach schnellen Implementierungen. Diese Arbeit untergliedert sich in drei Ansätze, die sich dieser Herausforderung widmen. Das umfasst vier Alignierungsalgorithmen und ihre Parallelisierung auf CUDA-fähiger Hardware, einen Algorithmus zur Segmentierung von Datenströmen und eine einheitliche Behandlung von Liegruppen-wertigen Zeitreihen.rnrnDer erste Beitrag ist eine vollständige CUDA-Portierung der UCR-Suite, die weltführende Implementierung von Teilsequenzalignierung. Das umfasst ein neues Berechnungsschema zur Ermittlung lokaler Alignierungsgüten unter Verwendung z-normierten euklidischen Abstands, welches auf jeder parallelen Hardware mit Unterstützung für schnelle Fouriertransformation einsetzbar ist. Des Weiteren geben wir eine SIMT-verträgliche Umsetzung der Lower-Bound-Kaskade der UCR-Suite zur effizienten Berechnung lokaler Alignierungsgüten unter Dynamic Time Warping an. Beide CUDA-Implementierungen ermöglichen eine um ein bis zwei Größenordnungen schnellere Berechnung als etablierte Methoden.rnrnAls zweites untersuchen wir zwei Linearzeit-Approximierungen für das elastische Alignment von Teilsequenzen. Auf der einen Seite behandeln wir ein SIMT-verträgliches Relaxierungschema für Greedy DTW und seine effiziente CUDA-Parallelisierung. Auf der anderen Seite führen wir ein neues lokales Abstandsmaß ein, den Gliding Elastic Match (GEM), welches mit der gleichen asymptotischen Zeitkomplexität wie Greedy DTW berechnet werden kann, jedoch eine vollständige Relaxierung der Penalty-Matrix bietet. Weitere Verbesserungen umfassen Invarianz gegen Trends auf der Messachse und uniforme Skalierung auf der Zeitachse. Des Weiteren wird eine Erweiterung von GEM zur Multi-Shape-Segmentierung diskutiert und auf Bewegungsdaten evaluiert. Beide CUDA-Parallelisierung verzeichnen Laufzeitverbesserungen um bis zu zwei Größenordnungen.rnrnDie Behandlung von Zeitreihen beschränkt sich in der Literatur in der Regel auf reellwertige Messdaten. Der dritte Beitrag umfasst eine einheitliche Methode zur Behandlung von Liegruppen-wertigen Zeitreihen. Darauf aufbauend werden Distanzmaße auf der Rotationsgruppe SO(3) und auf der euklidischen Gruppe SE(3) behandelt. Des Weiteren werden speichereffiziente Darstellungen und gruppenkompatible Erweiterungen elastischer Maße diskutiert.

Dynamics of Shifting Cultivation Landscapes in Northern Lao PDR Between 2000 and 2009 Based on an Analysis of MODIS Time Series and Landsat Images

The rotational nature of shifting cultivation poses several challenges to its detection by remote sensing. Consequently, there is a lack of spatial data on the dynamics of shifting cultivation landscapes on a regional, i.e. sub-national, or national level. We present an approach based on a time series of Landsat and MODIS data and landscape metrics to delineate the dynamics of shifting cultivation landscapes. Our results reveal that shifting cultivation is a land use system still widely and dynamically utilized in northern Laos. While there is an overall reduction in the areas dominated by shifting cultivation, some regions also show an expansion. A review of relevant reports and articles indicates that policies tend to lead to a reduction while market forces can result in both expansion and reduction. For a better understanding of the different factors affecting shifting cultivation landscapes in Laos, further research should focus on spatially explicit analyses.

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

Detecting offsets in GPS time series: first results from the Detection of Offsets in GPS Experiment

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. To assess the effectiveness of methods that detect and remove these offsets, we designed and managed the Detection of Offsets in GPS Experiment. We simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. The data set was made available to the GPS analysis community without revealing the offsets, and several groups conducted blind tests with a range of detection approaches. The results show that, at present, manual methods (where offsets are hand picked) almost always give better results than automated or semi‒automated methods (two automated methods give quite similar velocity bias as the best manual solutions). For instance, the fifth percentile range (5% to 95%) in velocity bias for automated approaches is equal to 4.2 mm/year (most commonly ±0.4 mm/yr from the truth), whereas it is equal to 1.8 mm/yr for the manual solutions (most commonly 0.2 mm/yr from the truth). The magnitude of offsets detectable by manual solutions is smaller than for automated solutions, with the smallest detectable offset for the best manual and automatic solutions equal to 5 mm and 8 mm, respectively. Assuming the simulated time series noise levels are representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than 0.2–0.4 mm/yr is therefore certainly not robust, although a limit of nearer 1 mm/yr would be a more conservative choice. Further work to improve offset detection in GPS coordinates time series is required before we can routinely interpret sub‒mm/yr velocities for single GPS stations.

Time-series analysis of Campylobacter incidence in Switzerland

SUMMARY Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.