Search

The change in the time series of non-scheduled passenger air transportation is random and variable, which creates a number of problems in forecasting the demand for this type of transportation. In calculations based on trend models, it is usually not possible to take into account all extraneous factors affecting non-scheduled passenger air transportation. For this reason, the accuracy and practical significance of the forecast are low. Considering the mentioned facts, this paper investigates the application of combined autoregressive integrated moving average (ARIMA) and support vector machine (SVM) methods to improve the accuracy of charter air transportation demand forecasting. ARIMA and SVM models usually complement each other in forecasting due to their inherent characteristics. These features include detecting temporal dependencies and trends, as well as handling non-linear relationships within historical data. The integration of these methods aims to obtain optimal forecast results using the time series analysis of the ARIMA model and the non-linear relationship detection feature of the SVM model. The obtained results emphasize the ability of ARIMA-SVM models to adapt to the dynamic demand patterns of non-scheduled air transportation and also offer a number of efficient ideas for the optimization of operational strategies and resource allocation in this field. The theoretical-practical results of this study, conducted with ARIMA and SVM methods, will be effective in the field of non-scheduled passenger air transportation. [ABSTRACT FROM AUTHOR]

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01–lag07, and lag03–lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65). [ABSTRACT FROM AUTHOR]

Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt as well as groundwater connectivity. In mountainous regions with limestone and dolomite geology, bedrock formations can host karst aquifers, which play a significant role in snowmelt–discharge dynamics. However, mapping complex karst features and the resulting surface‐groundwater exchanges at large scales remains infeasible. In this study, timeseries analysis of continuous discharge and specific conductance measurements were combined with gridded snowmelt predictions to characterize seasonal streamflow response and evaluate dominant watershed controls across 12 monitoring sites in a karstified 554 km2 watershed in northern Utah, USA. Immense surface water hydrologic variability across subcatchments, years and seasons was linked to geologic controls on groundwater dynamics. Unlike many mountain watersheds, the variability between subcatchments could not be well described by typical watershed properties, including elevation or surficial geology. To fill this gap, a conceptual framework was proposed to characterize subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds in terms of conduit flow direction, aquifer storage capacity and connectivity. This framework requires only readily measured surface water and climatic data from nested monitoring sites and was applied to the study watershed to demonstrate its applicability for evaluating dominant controls and climate sensitivity. [ABSTRACT FROM AUTHOR]

Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the "8.2 ka" event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. Key Points: Laplacian eigenmaps of recurrence matrices (LERM) is a novel tool for paleoclimate time series analysisLERM can robustly detect the gradual Mid‐Pleistocene Transition in relatively low signal‐to‐noise ratio scenariosLERM can also be applied to detect abrupt climate transitions like the 8.2 ka event, though less robustly [ABSTRACT FROM AUTHOR]

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01–lag07, and lag03–lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

Measures of association, which typically require pairwise data, are widespread in many aspects of educational research. However, due to the need to reduce their content to equal numbers of units of analysis, they are rarely found in the analysis of textbooks. In this paper, we present two methods for overcoming this limitation, one through the use of disjoint sections and the other through the use of overlapping moving averages. Both methods preserve the temporal structure of data and enable researchers to calculate a measure of association which, in this case, is the complementary Euclidean average distance, as an indicator of the books' similarity. We illustrate these approaches by means of a comparative analysis of three commonly-used English and Swedish mathematics textbooks. Analyses were focused on individual tasks, which had all been coded according to the presence or absence of particular characteristics. Both methods produce nearly identical results and are robust with respect to both densely and sparsely occurring characteristics. For both methods, widening the aggregation window results in a slightly increased level of quantified similarity, which is the result of the 'smoothing effect'. We discuss the relation between the window width and the choice of research question. [ABSTRACT FROM AUTHOR]

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders. [ABSTRACT FROM AUTHOR]

In this study, the BFAST model was used to decompose MODIS NDVI time-series and detect negative abrupt changes within trend during the study period (2005 to 2018). These negative abrupt changes were filtered and assigned as disturbances in the selected forest classes. The results showed that on average 15.01% of the forests were disturbed annually and 86.84% area was affected during the whole study period. The selected forests were affected the most in year 2008, 2011, and 2016. The model validation established a strong agreement between measured and estimated disturbance with 92.63% overall accuracy and kappa coefficient value of 0.86. The information about the time, extent, and impact of disturbances derived from remote sensing and GIS might be a critical tool for the degradation evaluation of forest ecosystem and also help in the development of forest management policies for sustainable development of the Himalayan region. [ABSTRACT FROM AUTHOR]

We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries, Pyleoclim. The code is open‐source, object‐oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code's philosophy, structure, and base functionalities and apply it to three paleoclimate problems: (a) orbital‐scale climate variability in a deep‐sea core, illustrating spectral, wavelet, and coherency analysis in the presence of age uncertainties; (b) correlating a high‐resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (c) model‐data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting Findable, Accessible, Interoperable, and Reusable software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud‐executable Jupyter notebooks, to encourage adoption by new users. Plain Language Summary: This article describes a software application called Pyleoclim meant to help scientists analyze datasets of ordered observations, particularly applicable to the study of past climates, environments, and ecology. Pyleoclim is meant to be used by domain scientists as well as students interested in learning more about Earth's climate through examples provided in the documentation and online tutorials. Pyleoclim is intended to help scientists save time with their analyses, documenting the steps for better transparency, and as such, allows other scientists to reproduce results from previous studies. Key Points: Pyleoclim makes timeseries analysis tools accessible to practicing scientists, via a user‐friendly Python packageThree Jupyter Notebooks illustrate how Pyleoclim facilitates common and advanced tasksPyleoclim can enhance reproducibility and rigor of paleogeoscientific workflows involving timeseries [ABSTRACT FROM AUTHOR]

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days' worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies' findings of the efficacy of open-source AID. [ABSTRACT FROM AUTHOR]

Contemporary mine exploitation requires information about the deposit itself and the impact of mining activities on the surrounding surface areas. In the past, this task was performed using classical seismic and geodetic measurements. Nowadays, the use of new technologies enables the determination of the necessary parameters in global coordinate systems. For this purpose, the relevant services create systems that integrate various methods of determining interesting quantities, e.g., seismometers / GNSS / PSInSAR. These systems allow detecting both terrain deformations and seismic events that occur as a result of exploitation. Additionally, they enable determining the quantity parameters that characterise and influence these events. However, such systems are expensive and cannot be set up for all existing mines. Therefore, other solutions are being sought that will also allow for similar research. In this article, the authors examined the possibilities of using the existing GNSS infrastructure to detect seismic events. For this purpose, an algorithm of automatic discontinuity detection in time series "Switching Edge Detector" was used. The reference data were the results of GNSS measurements from the integrated system (seismic / GNSS / PSI nSA R) installed on the LGCB (Legnica-Głogów Copper Belt) area. The GNSS data from 2020 was examined, for which the integrated system registered seven seismic events. The switching Edge Detector algorithm proved to be an efficient tool in seismic event detection. [ABSTRACT FROM AUTHOR]

BackgroundAfter most COVID-19 pandemic control measures were lifted in 2022, many infectious diseases re-emerged. An increase in invasive group A streptococcal (iGAS) infections among adults and young children was reported by several countries. Viral infections including influenza and varicella, known risk factors for iGAS infection, also increased.AimTo estimate the proportion of GAS skin and soft tissue infections (SSTI) and pneumonia/sepsis in children (≤ 5 years) attributable to varicella, and the proportion of GAS pneumonia/sepsis in children and adults attributable to potentially predisposing respiratory viruses influenza A and B, RSV, hMPV and SARS-CoV-2 in the Netherlands.MethodsWe performed time series regression using weekly data on respiratory viruses, varicella and non-invasive GAS infections and GAS isolates cultured from blood, lower airways, skin, pus and wounds, from January 2010 to March 2023.ResultsIn 2010-19, 50% (95% CI: 36-64) of GAS SSTI in children were attributable to varicella. Between January 2022 and March 2023, 34% (95% CI: 24-43) of GAS SSTI cases were attributable to varicella. Of iGAS pneumonia/sepsis between January 2022 and March 2023, 34% (95% CI: 20-49) and 25% (95% CI: 18-32) was attributable to respiratory virus infections in children and adults, respectively, with the largest contributor (17%) being influenza A.ConclusionsPredisposing viral infections likely contributed to, but cannot fully explain, the observed iGAS increase among children and adults in 2022-23 in the Netherlands. Public health measures to control viral infections, such as vaccination against varicella or influenza, might reduce the iGAS disease burden.

Methodologies for rainfall‐runoff event identification from continuous time series suffer from significant subjectivity. In particular, whether they initiate the identification from rainfall or from the streamflow timeseries, they usually require baseflow separation and they need substantial modifications and parameters' recalibration when changing temporal resolution of the data. Therefore, here we propose a novel objective methodology for event identification that is easily transferable across sites and temporal resolutions, without having to make subjective choices and adjust multiple parameters. The proposed method to identify rainfall‐runoff events is based on a time series analysis technique that simultaneously considers rainfall and streamflow time series and does not make any a priori assumptions about baseflow separation. The novel method allows also to produce a baseflow separation a posteriori by connecting the delimiters of identified streamflow events. Moreover, the proposed method can be applied at any time resolution as long as the resolution is high enough to capture the time delay between precipitation and runoff response. When comparing the results between the proposed and the traditional baseflow‐based event identification approach, we observe a good agreement in terms of event properties both at hourly and daily scale (correlation of runoff ratios between the two methods equal to 0.78 [daily data] and 0.84 [hourly data]). The analysis comparing hourly and daily event identifications with the proposed method reveals also that the novel method produces coherent events across different temporal resolutions (correlation of runoff ratios between daily and hourly data equal to 0.71). Key Points: The proposed methodology compares well with a more traditional baseflow‐based event identification approachThe proposed method does not require any a priori baseflow separation and allows to produce a baseflow separation a posterioriThe proposed method can be consistently applied to different time resolutions of the data [ABSTRACT FROM AUTHOR]

The issue of PM2.5 pollution has received significant attention in the literature as it has social, economic, and political implications. Big data sets have been collected by pollution monitoring stations (i.e., nodes) throughout the world, and this has made it possible to quantitatively characterize the dependence of PM2.5 pollution in different regions. Here we divide the dependency relationship into three types: association, correlation, and causation. This study conducted such relationships using three approaches: the random matrix theory (RMT), cross-correlation, and convergent cross-mapping (CCM). The aim of this study is to determine the above three relationships between pollution data from different nodes. A random matrix analysis revealed that pollutant time series are not completely random, but are associated. Further analysis showed that PM2.5 sequences had clear short-range correlations, yet the long-range correlations were blurred. Moreover, at the collect level, there were no clear causalities among pollutant concentrations from different geographical regions, regardless of distance and direction. These results indicate that the dependence of PM2.5 pollution between different sites is complex. Nonetheless, this comprehensive analysis based on big data provided insights into critical issues of general interest, including pollution-induced climate change, and pollution abatement.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the world's largest public health concern in 2021. This study evaluated the associations of the prevalence of airway symptoms among the tested individuals and data regarding the natural environmental factors with the weekly number of newly diagnosed COVID-19 patients in Sendai City (Nt). For the derivatives of the screening test results, data from individuals with a contact history who underwent nasopharyngeal swab reverse transcription-polymerase chain reaction (RT-PCR) testing between July 2020 and April 2021 (6,156 participants, including 550 test-positive patients) were used. The value of Nt correlated with the weekly RT-PCR test-positive rate after close contact, prevalence of cough symptoms in test-positive individuals or in test-negative individuals, lower air temperature, lower air humidity, and higher wind speed. The weekly test-positive rate correlated with lower air humidity and higher wind speed. In cross-correlation analyses, natural environmental factors correlated with the regional epidemic status on a scale of months, whereas the airway symptoms among non-COVID-19 population affected on a scale of weeks. When applying an autoregression model to the serial data of Nt, large-scale movements of people were suggested to be another factor to influence the local epidemics on a scale of days. In conclusion, the prevalence of cough symptoms in the local population, lower air humidity or higher wind speed, and largescale movements of people in the locality would jointly influence the local epidemic status of COVID-19. [ABSTRACT FROM AUTHOR]

This work presents a modular Smart Maintenance Platform (SMP), which offers a toolbox of condition monitoring tools for the day-to-day inspection of the machinery equipment in a real industrial environment. SMP complements the manual inspection performed by the maintenance engineers with advanced data-analytics solutions that utilize both reactive and proactive techniques based on state-of-the-art machine learning proposals. Appropriate data acquisition mechanisms are built to acquire the sensorial data and transform them to suitable forms for data processing. Higher-level monitoring policies are enabled, through the fusion of multiple monitoring tasks increasing the precision of the results. The generated results are persisted for the continuous enhancement of the solution through the retraining of the proactive approach or the re-configuration of the reactive ones. The generated results are visualized in a user-friendly format, to help the maintenance engineers to assess the severity of the situation and to intervene in the machinery equipment if deemed necessary. [ABSTRACT FROM AUTHOR]

Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.

This paper aims at being a provocative guide to the future of asteroseismology from the perspective of the analysis of time series, where the fundamentals of harmonic analysis are subjected to stress tests. In this context, we give an annotated summary of our research over the last decades on harmonic analysis of A-F stars. We discuss and explore the consequences of our findings, which may extend to any kind of pulsators. As well, we analyse the impact of this reconsideration on future asteroseismic studies, which would entail a paradigm shift. This includes a discussion on the presence of fractal behavior in δ Sct stars, and how this can be used to develop a stopping criterion of the pre-whitening process, as an alternative to SNR (or significance) criterion. Drilling a scientific paradigm has its natural resilience, hence the path described here is being arduous, although fruitful at the same time.

The Callovo-Oxfordian claystone is a material with notoriously complex hydro-mechanical behaviour. Combined neutron and x-ray tomography modalities are used for the first time to characterise the dynamics of water absorption in this material by comparing material deformation as well as water arrival. Exploiting recent work on multimodal registration, neutron, and x-ray datasets are registered pairwise into a common coordinate system, meaning that a vector-valued field (i.e., neutron and x-ray reconstructed values) is available for each timestep, essentially making this a 5D dataset. The ability to cross-plot each field into a joint histogram (an inherent input into the registration) allows an improved identification of mineral phases in this complex material. Material deformation obtained from the application of Digital Volume Correlation on the x-ray timeseries data is locally compared to changes in water content available from the neutrons, opening the way toward a quantitative description of the hydro-mechanics of this process.

In the last decades, major advances in the language sciences have been built on real-time measures of language and cognitive processing, measures like mouse-tracking, event related potentials and eye-tracking in the visual world paradigm. These measures yield densely sampled timeseries that can be highly revealing of the dynamics of cognitive processing. However, despite these methodological advances, existing statistical approaches for timeseries analyses have often lagged behind. Here, we present a new statistical approach, the Bootstrapped Differences of Timeseries (BDOTS), that can estimate the precise timewindow at which two timeseries differ. BDOTS makes minimal assumptions about the error distribution, uses a custom family-wise error correction, and can flexibly be adapted to a variety of applications. This manuscript presents the theoretical basis of this approach, describes implementational issues (in the associated R package), and illustrates this technique with an analysis of an existing dataset. Pitfalls and hazards are also discussed, along with suggestions for reporting in the literature. [ABSTRACT FROM AUTHOR]

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data. [ABSTRACT FROM AUTHOR]

The rank-summultiple change-point method is a robust statistical procedure designed to search for the optimal number and the location of change points in an arbitrary continue or discrete sequence of values. As such, this procedure can be used to analyse time-series data. Twelve years of robust data sets for the Séchilienne (French Alps) rockslide showa continuous increase in average displacement rate from 50 to 280 mm per month, in the 2004-2014 period, followed by a strong decrease back to 50 mm per month in the 2014-2015 period. When possible kinematic phases are tentatively suggested in previous studies, its solely rely on the basis of empirical threshold values. In this paper, we analyse how the use of a statistical algorithm for change-point detection helps to better understand time phases in landslide kinematics. First,we test the efficiency of the statistical algorithm on geophysical benchmark data, these data sets (stream flows and Northern Hemisphere temperatures) being already analysed by independent statistical tools. Second, we apply the method to 12-yr daily time-series of the Séchilienne landslide, for rainfall and displacement data, from 2003 December to 2015 December, in order to quantitatively extract changes in landslide kinematics. We find two strong significant discontinuities in the weekly cumulated rainfall values: an average rainfall rate increase is resolved in 2012 April and a decrease in 2014 August. Four robust changes are highlighted in the displacement time-series (2008 May, 2009 November-December-2010 January, 2012 September and 2014 March), the 2010 one being preceded by a significant but weak rainfall rate increase (in 2009 November). Accordingly, we are able to quantitatively define five kinematic stages for the Séchilienne rock avalanche during this period. The synchronization between the rainfall and displacement rate, only resolved at the end of 2009 and beginning of 2010, corresponds to a remarkable change (fourfold increase in mean displacement rate) in the landslide kinematic. This suggests that an increase of the rainfall is able to drive an increase of the landslide displacement rate, but that most of the kinematics of the landslide is not directly attributable to rainfall amount. The detailed exploration of the characteristics of the five kinematic stages suggests that the weekly averaged displacement rates are more tied to the frequency or rainy days than to the rainfall rate values. These results suggest the pattern of Séchilienne rock avalanche is consistent with the previous findings that landslide kinematics is dependent upon not only rainfall but also soil moisture conditions (as known as being more strongly related to precipitation frequency than to precipitation amount). Finally, our analysis of the displacement rate time-series pinpoints a susceptibility change of slope response to rainfall, as being slower before the end of 2009 than after, respectively. The kinematic history as depicted by statistical tools opens new routes to understand the apparent complexity of Séchilienne landslide kinematic. [ABSTRACT FROM AUTHOR]

While the concept of connectivity has gained popularity in fields like hydrology and ecology, little agreement exists on its definition, which hinders its use in both scientific and legal contexts. In contrast, neuroscientists have developed not only strong conceptualizations of connectivity but also tools to quantify it: a clear distinction is made between structural connectivity, which is determined from brain anatomy; functional connectivity, which is estimated based on statistical dependencies between neuronal electric timeseries; and effective connectivity, which infers causal relations from the same timeseries based on the assumption that “true” interactions occur with a certain time delay. The motivation of this review arose from the hypothesis that connectivity-related statistical techniques, which are applied to timeseries of electrical currents measured by placing electrodes on the scalp of the human brain, could also apply to high-frequency hydrological timeseries acquired to characterize catchment response to precipitation. Here we bring together existing conceptualizations of structural, functional and effective connectivity in hydrology and ecology and compare them with those used in brain neuroscience. We then summarize the most important brain connectivity measures and their associated mathematical frameworks before evaluating the potential of those measures to help advance our understanding of hydrologic connectivity properties – in terms of the frequency, magnitude, timing, duration and rate of water movement linking two disparate locations. Lastly, we present a short case study where a selection of brain connectivity measures is applied to 35 groundwater and streamflow timeseries from a Swiss catchment to infer subsurface flow-driven hydrologic connectivity. Our literature review combined with our short case study suggest that an ensemble of functional and effective connectivity measures should be used and constrained not only by structural connectivity measures but also by interpretation thresholds in order to make results parsimonious. We highlight challenges associated with transferring brain connectivity measures to hydrology, especially those related to choosing the appropriate length and sampling frequency of input timeseries when assessing perennial versus ephemeral connectivity, appropriately detecting and differentiating noisy from indirect connections, and interpreting unbounded connectivity measures. We then offer recommendations for future research and propose that hydrologists use a common classification system encompassing all potential connectivity assessment approaches and measures in order to facilitate scientific communication. [ABSTRACT FROM AUTHOR]

Radar Interferometry is a widely used method for estimating ground deformation, as it provides precision to a few millimeters to centimeters, and at the same time, a wide spatial coverage of the study area. On 9 July 1956, one of the strongest earthquakes of the 20th century in the area of the South Aegean, occurred in Amorgos, with a magnitude of Mw = 7.7. The objective of this research is to map ground deformation in Amorgos island, using InSAR techniques. We conducted a multi-temporal analysis of all available data from 2003 to 2019 by exploiting historical ENVISAT SAR imagery, as well as the dense archive of Sentinel-1 SLC imagery. Persistent Scatterer Interferometry (PS) and Small Baseline Subset (SBAS) methods were implemented. Results of both data-sets indicate a small-scale deformation on the island. A multi-track analysis was implemented on Sentinel-1 data to decompose the line of sight velocities to vertical and horizontal. The central south coast is experiencing horizontal movement, while uplift of a maximum value of 5 mm/y is observed in the southeastern coast. The combination of the good spatial coverage achievable via InSAR, with GPS measurements, is suggested an important tool for the seamless monitoring of Amorgos island towards tectonic hazard estimation.

This analysis introduces a generalization of the basic statistical concepts of expectation values and variance for non-Euclidean metrics induced by Lp-norms. The non-Euclidean Lp means are defined by exploiting the fundamental property of minimizing the Lp deviations that compose the Lp variance. These Lp expectation values embody a generic formal scheme of means characterization. Having the p-norm as a free parameter, both the Lp-normed expectation values and their variance are flexible to analyze new phenomena that cannot be described under the notions of classical statistics based on Euclidean norms. The new statistical approach provides insights into regression theory and Statistical Physics. Several illuminating examples are examined.

The behavioural data yielded by single subjects in naturalistic and controlled settings likely contain valuable information to scientists and practitioners alike. Although some of the properties unique to this data complicate statistical analysis, progress has been made in developing specialised techniques for rigorous data evaluation. There are no perfect tests currently available to analyse short autocorrelated data streams, but there are some promising approaches that warrant further development. Although many approaches have been proposed, and some appear better than others, they all have some limitations. When data sets are large enough (∼30 data points per phase), the researcher has a reasonably rich pallet of statistical tools from which to choose. However, when the data set is sparse, the analytical options dwindle. Simulation modelling analysis (SMA; described in this article) is a relatively new technique that appears to offer acceptable Type-I and Type-II error rate control with short streams of autocorrelated data. However, at this point, it is probably too early to endorse any specific statistical approaches for short, autocorrelated time-series data streams. While SMA shows promise, more work is needed to verify that it is capable of reliable Type-I and Type-II error performance with short serially dependent streams of data. [ABSTRACT FROM AUTHOR]

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Toward this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter’s social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex. [ABSTRACT FROM AUTHOR]

Abstract: Cyclical patterns of behaviour in timeseries of seismic and geodetic data at volcanoes are frequently observed during lava dome-building eruptions, and are particularly well-documented from the current eruption of the Soufrière Hills Volcano (SHV), Montserrat. However, the discontinuous nature of many SO2 measurements often preclude the identification and quantitative analysis of cyclical patterns in degassing data. Here, using a long SO2 timeseries from SHV, with continuous measurements since 2002, we explore for the first time degassing behaviour at a resolution comparable to that possible for seismic and deformation datasets. Timeseries analysis of flux data spanning 2002–2011 reveals that SO2 emissions at SHV exhibit complex cyclicity, with dominant cycles evident on both multi-year and multi-week (~50 day) timescales. These cycles persist through phases of both active extrusion and eruptive pause, and show close similarities to periodic components previously identified at SHV in timeseries of seismicity, ground deformation and lava extrusion. The strength of expression or amplitude of degassing cycles, particularly on multi-week timescales, shows distinct temporal variation, and appears to correlate with the occurrence and nature of explosive activity occurring in 2002–2009. This suggests that the amplitude of surface gas flux cycles is modulated by physical conditions within the conduit. Direct quantitative comparison between seismicity, dome growth, and degassing for eruptive Phases 2 (2002–2003) and 3 (2005–2007) reveals that peaks in SO2 flux appear to correspond broadly to enhanced lava extrusion and elevated seismicity within cycles of 30–50 days. However, time lags of 2, 4 and 7 days are observed between initial low-frequency seismic swarms and peaks in dome growth, SO2 flux and rockfall event rate respectively. Multi-parameter correlations offer valuable insights into the controls on subsurface gas ascent, but further research is required to fully explore the contributions of permeability and overpressure, as well as other subsurface processes. [Copyright &y& Elsevier]

This discussion does not go into specific statistical details. It expresses the impressions of a climatologist when reading a paper about statistical methodologies applied on climate timeseries. Thus the main goal of the discussion is a typical interdisciplinary one. Some of the statistical results of the Battaglia and Protopapas paper are commented versus findings and also questions arising from the underlying physics of the climate system including also some climate impacts. [ABSTRACT FROM AUTHOR]

Abstract: We explore the use of block entropy as a dynamics classifier for meteorological timeseries data. The block entropy estimates define the entropy growth curve H(L) with respect to block length L. For a finitary process, the entropy growth curve tends to an asymptotic linear regime H(L) = E + hμL, with entropy rate hμ and excess entropy E. These quantities apportion the system''s information content into ‘memory’ (E) and ‘randomness’ (hμ). We discuss the challenges inherent in analyzing weather data using symbolic techniques, identifying the pitfalls associated with alphabet size, finite sample timeseries length, and stationarity. We apply the block entropy-based techniques in the form of a wet/dry partition to Australian daily precipitation data from the Patched Point Dataset station record collection and version 3 of the Australian Water Availability Project analysis dataset. Preliminary results demonstrate hμ and E are viable climatological classifiers for precipitation, with station records from similar climatic regimes possessing similar values of hμ and E. The resultant clustering reflects expected characteristics of local climatic memory and randomness. The AWAP results show weaker clustering than their PPD counterparts, with different E- and hμ-values reflecting respectively the relative biases and truncation errors in the AWAP analysis system. The entropy rates of convergence analysis rules out finite order Markov processes for orders falling within the range of block sizes considered. [Copyright &y& Elsevier]

The objective of this paper is to evaluate the degree of financial integration achieved in the European Union based on covered interest parity and using Generalized Maximum Entropy. EU countries are divided into two groups according to their current situation with respect to the adoption of the euro. Financial integration before the adoption of the euro is analyzed for the countries that adopted the euro in 1999. Similarly, current financial integration is evaluated for non-euro EU countries. Besides the importance of comparing the situation of the non-euro EU countries with the situation of the euro EU countries previous to the euro adoption, which may be useful to evaluate an eventual decision of the non-euro members to adopt the euro, it is interesting to analyze the performance of Generalized Maximum Entropy. Generalized Maximum Entropy has the ability to estimate the parameters of a regression model without imposing any constrains on the probability distribution of errors and it is robust even when we have ill-posed problems. Overall our results suggest that the degree of financial integration on non-euro countries is lower than the degree of financial integration that existed among euro adopting countries before the adoption of the euro. [ABSTRACT FROM AUTHOR]

• We developed a Budyko-based approach for detection of fire effects on streamflow. • We attributed changes in streamflow in post-fire years to fire and climatic causes. • Hydrologic recovery ranged between 5 and 45 years, increasing with increased burned area. Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko "decomposition" method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr−1, though the effect varied greatly between years. Finally, we estimated hydrologic recovery timescales that varied between 5 and 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned. [ABSTRACT FROM AUTHOR]

Abstract: Construction of stochastic models that describe the effective dynamics of observables of interest is an useful instrument in various fields of application, such as physics, climate science, and finance. We present a new technique for the construction of such models. From the timeseries of an observable, we construct a discrete-in-time Markov chain and calculate the eigenspectrum of its transition probability (or stochastic) matrix. As a next step we aim to find the generator of a continuous-time Markov chain whose eigenspectrum resembles the observed eigenspectrum as closely as possible, using an appropriate norm. The generator is found by solving a minimization problem: the norm is chosen such that the object function is quadratic and convex, so that the minimization problem can be solved using quadratic programming techniques. The technique is illustrated on various toy problems as well as on datasets stemming from simulations of molecular dynamics and of atmospheric flows. [Copyright &y& Elsevier]

1. Attempts to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the density-dependent signal may in reality be weak or absent. 4. We distinguish two forms of measurement error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without measurement error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods. [ABSTRACT FROM AUTHOR]

An iterative solution is developed for the calculation of the best achievable (minimum variance) PID control performance and the corresponding optimal PID setting in an existing control loop. An analytic expression is derived for the closed-loop output as an explicit function of PID setting. The resulting benchmark allows for realistic performance assessment of an existing PID control loop, especially when the control loop fails to meet the minimum variance performance. A PID performance index is then defined based on the PID performance bound, and its confidence interval is estimated. A series of simulated examples are used to demonstrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

We study the temporal variability of human brain activity in timeseries of functional magnetic resonance imaging (fMRI) data. We find that these timeseries show scaling behavior which is quantified by computing various scaling exponents. We demonstrate that mentally active zones are one-to-one related to large exponents, or equivalently, to highly temporally correlated processes, while mentally inactive zones are well described by a simple random walk model. Activation maps of scaling exponents are presented and compared to standard results in fMRI. In contrast to standard model-based activation analyses no prior knowledge of the experimental stimulation paradigm has to be assumed for extracting activation patterns from fMRI timeseries. We mention consequences of persistence in fMRI timeseries for standard statistical analysis. [Copyright &y& Elsevier]