Comparison Of Time Series Research Articles

Влияние изменений потока космических лучей на общую циркуляцию атмосферы

A comparison of average daily values time series of the axial component of the global wind angular moment and galactic cosmic ray (GCR) flux was made. Using the superposed epoch method it is shown that the Forbush decreases of the GCR flux are accompanied by an increase in the average angular velocity of atmosphericcirculation and, accordingly, by an increase in the zonal transfer.

Forecasting inflation in Turkey: A comparison of time-series and machine learning models

Purpose: This paper aims to test the accuracy of some Machine Learning (ML) models in forecasting inflation in the case of Turkey and to give a new and also complementary approach to time series models. Methods: This paper forecasts inflation in Turkey by using time-series and machine learning (ML) models. The data is spanning from the period 2006:M1 to 2020:M12. Findings: According to our findings, although the linear-based Ridge and Lasso regression algorithms perform worse than the VAR model, the multilayer perceptron algorithm gives satisfactory results that are close to the results of the time series algorithm. In this direction, non-linear machine learning models are thought to be a reliable complementary method for estimating inflation in emerging economies. It is also predicted that it can be considered as an alternative method as the amount of data and computational power increase. Implication: The findings are expected to be useful as a guide for central banks and policy-makers in emerging economies with volatile inflation rates. Originality: We evaluate the forecasting performance of ML models against each other and a time series model, and investigate possible improvements upon the naive model. So, this is the first study in the field, which uses both linear and nonlinear ML methods to make a comparison with the time series inflation forecasts for Turkey.

Ambient Infrasound Noise, Station Performance, and Their Relation to Land Cover across Alaska

AbstractThe addition of 108 infrasound sensors—a legacy of the temporary USArray Transportable Array (TA) deployment—to the Alaska regional network provides an unprecedented opportunity to quantify the effects of a diverse set of site conditions on ambient infrasound noise levels. TA station locations were not chosen to optimize infrasound performance, and consequently span a dramatic range of land cover types, from temperate rain forest to exposed tundra. In this study, we compute power spectral densities for 2020 data and compile new ambient infrasound low- and high-noise models for the region. In addition, we compare time series of root-mean-squared (rms) amplitudes with wind data and high-resolution land cover data to derive noise–wind speed relationships for several land cover categories. We observe that noise levels for the network are dominated by wind, and that network noise is generally higher in the winter months when storms are more frequent and the microbarom is more pronounced. Wind direction also exerts control on noise levels, likely as a result of infrasound ports being systematically located on the east side of the station huts. We find that rms amplitudes correlate with site land cover type, and that knowledge of both land cover type and wind speed can help predict infrasound noise levels. Our results show that land cover data can be used to inform infrasound station site selection, and that wind–noise models that incorporate station land cover type are useful tools for understanding general station noise performance.

A research on the role of women in economic development in the BRICS countries

Women account for almost half of the world population, however, their contribution to the economic development is rather limited due to significant gender imparity manifested in different aspects: from participation in decision-making and employment to incomes and property. The paper aims to explore and assess the potential of women empowerment to the benefits of economic growth in the BRICS countries. Methodologically, the paper relies on the theory of gender order and the interdisciplinary approach to the research of gender inequality. The study applies methods of systematisation, content analysis, and comparison of time series. By analysing a range of international organisations’ reports, the paper provides insight in the state of affairs with gender inequality in the BRICS countries looking at its key characteristics in the economic, educational, social, and political spheres. Using the World Bank data for 2000–2021, the paper examines women’s empowerment according to the dimensions accounted for in the UN Gender Inequality Index: empowerment, health, and labour market. The findings indicate that the BRICS countries are far behind in terms of delivering real justice to women’s empowerment. Among the five countries China and Russia seem to be putting the strongest efforts to reduce gender gap, Brazil demonstrates good dynamics, but has a long way to go, while South Africa and India confront many deep-rooted problems that strongly thwart their progress. The theoretical and practical significance of the study lies in the consolidation of the data on the problem of gender imparity as well as their interpretation in the context of supporting gender equal societies in the BRICS countries to promote the global spatial economic development.

Opportunity Seeking Across Segregated Schools: Unintended Effects of Automatic Admission Policies on High School Segregation

Automatic admissions policies (AAPs, “percent plans”) redistribute college-going opportunities across segregated high schools to diversify college enrollments, increasing opportunities at predominantly minority high schools. If students “game” AAPs by attending schools with increased opportunities, AAPs could alter racial sorting across high schools. Comparative interrupted time series analyses provide evidence that Texas’s and California’s AAPs reduced Black–White segregation in highly segregated school districts. These effects were concentrated in sparsely populated areas in Texas, and they were modest in California, so it seems unlikely this significantly undermined AAPs’ ability to reduce racial disparities in college-going opportunities. It shows, however, that strategic responses to policies that redistribute opportunities in segregated contexts can create tension between segregation and inequality of opportunity.

Coral reef & temperate coastal soundscape features evident in directional and omnidirectional passive acoustic time series

Biologically complex coastal environments, such as coral reefs, demonstrate an equally rich ambient soundscape. Bioacoustic features of coastal soundscapes are closely tied with relative ecosystem health, functional groups present, and can be linked with specific behaviors. Biological contributions to ambient soundscapes have distinctive qualities as compared to sound associated with physical processes (i.e. wind and wave noise). While some biological components are readily identifiable, such as marine mammal or fish calls, the background noise associated with hundreds of thousands of biological clicks, snaps, and pops is not as well studied but contains a wealth of information about the ecosystem. A 64-element line array with 4.5 kHz design frequency was deployed for several field experiments off the coast of Kona, Hawaii in 2019 and 2020. Soundscape data from Hawaii were compared with comparable omnidirectional time series from Bermuda (2020) and coastal New England rocky reefs (2020–2021). Similarities in certain spectral features associated with biological sound sources were found between these unique ecosystems. The characteristic coral reef evening chorus, or significant increase in sound levels immediately prior to sunset, was consistent in Hawaii and Bermuda with comparable crepuscular changes in coastal New England.

Medicare Durable Medical Equipment Competitive Bidding Program: An Evaluation.

The Centers for Medicare and Medicaid Services (CMS) implemented the Medicare durable medical equipment (DME) Competitive Bidding Program (CBP) in 2011. Since then, concerns have been raised regarding access to equipment and adverse health outcomes. The aim was to evaluate whether the CBP was associated with changes in spending, utilization, and adverse health events (emergency department visits, hospitalizations, and falls). A comparative interrupted time series over 8 years was used to compare Round1 and Round2 bidding to nonbidding areas. Medicare fee for services claims were aggregated at the quarterly Metropolitan Statistical Area (MSA) level from 2009 to 2016. For the 3 evaluated DME (continuous positive airway pressure machines, oxygen supplies, and walkers), we found that implementation of the Medicare CBP was associated with reductions in per capita spending without changes in DME utilization or adverse health outcomes in CBP areas compared with nonbidding areas. For example, the slope change in the proportion of oxygen supplies purchasers in Round1 areas after implementation of Round1 was similar to the slope change in nonbidding areas (-0.0002; 95% CI: -0.0004, 0.0001; P=0.189). The difference in slope changes of emergency department visits and hospitalization in Round1 areas for oxygen supplies were (-0.0004; 95% CI: -0.0016, 0.0008; P=0.514) and (0.0002; 95% CI: -0.0010, 0.0014; P=0.757), respectively. Findings in Round2 areas after implementation of Round2 were similar to findings in Round1 areas. The Medicare DME CBP lowered Medicare expenditures while not reducing beneficiary access or increasing adverse outcomes.

The Internal Validity of the School-Level Comparative Interrupted Time Series Design: Evidence From Four New Within-Study Comparisons

Comparative interrupted time series (CITS) designs evaluate impact by modeling the relative deviation from trends among a treatment and comparison group after an intervention. The broad applicability of the design means it is widely used in education research. Like all non-experimental evaluation methods however, the internal validity of a given CITS evaluation depends on assumptions that cannot be directly verified. We provide an empirical test of the internal validity of CITS by conducting four within-study comparisons of school-level interventions previously evaluated using randomized controlled trials. Our estimate of bias across these four studies is 0.03 school-level (or 0.01 pupil-level) standard deviations. The results suggest well-conducted CITS evaluations of similar school-level education interventions are likely to display limited bias.

A visualization technique to assist in the comparison of large meteorological datasets

The comparison of meteorological data is a fundamental task within the techniques of meteorological forecasting due to the cyclical nature of the climate. However, interpretation of meteorological datasets can be difficult due to their large size. The goal of information visualization is to support a better understanding of the data what includes assisting the user in the data comparison process. However, few visualization techniques have been specifically developed to support the comparison process of big meteorological data. In this paper, we improved a visualization technique for large time-series comparison. The new technique is more suitable for the comparison of meteorological data.

City-Wide Firearm Violence Spikes in Minneapolis following the Murder of George Floyd: A Comparative Time-Series Analysis of Three Cities

This study investigates the aftermath of a high-profile violent police incident as it relates to city-wide firearm violence. Utilizing two Midwest cities (Kansas City, Missouri and Omaha, Nebraska) as comparison cities, we assess whether violent firearm incidents increased in Minneapolis after the murder of George Floyd. Multiple interrupted time-series analyses showed statistically significant increases in weekly firearm incidents in Minneapolis (AME = 10.63, p < 0.05) and Omaha (AME = 1.47, p < 0.5) following the murder of George Floyd. No significant results were found in Kansas City. Similar relationships were found when examining monthly firearm incidents. The firearm spike in Minneapolis thus represents an approximate 10-fold increase in weekly firearm incidents relative to that observed in Omaha. We conclude, therefore, that the murder of George Floyd was associated with a substantially greater increase in firearm violence in Minneapolis than in the two comparison cities. Police training to reduce police violence and public health approaches to reduce urban firearm violence will alleviate the social and economic impacts of violence on federal and state governments.

The comparative interrupted time series design for assessment of diagnostic impact: methodological considerations and an example using point-of-care C-reactive protein testing

BackgroundIn diagnostic evaluation, it is necessary to assess the clinical impact of a new diagnostic as well as its diagnostic accuracy. The comparative interrupted time series design has been proposed as a quasi-experimental approach to evaluating interventions. We show how it can be used in the design of a study to evaluate a point-of-care diagnostic test for C-reactive protein in out-of-hours primary care services, to guide antibiotic prescribing among patients presenting with possible respiratory tract infection. This study consisted of a retrospective phase that used routinely collected monthly antibiotic prescribing data from different study sites, and a prospective phase in which antibiotic prescribing rates were monitored after the C-reactive protein diagnostic was introduced at some of the sites.MethodsOf 8 study sites, 3 were assigned to receive the diagnostic and 5 were assigned as controls. We obtained retrospective monthly time series of respiratory tract targeted antibiotic prescriptions at each site. Separate ARIMA models at each site were used these to forecast monthly prescription counts that would be expected in the prospective phase, using simulation to obtain a set of 1-year predictions alongside their standard errors. We show how these forecasts can be combined to test for a change in prescription rates after introduction of the diagnostic and estimate power to detect this change.ResultsFitted time series models at each site were stationary and showed second-order annual seasonality, with a clear December peak in prescriptions, although the timing and extent of the peak varied between sites and between years. Mean one-year predictions of antibiotic prescribing rates based on the retrospective time series analysis differed between sites assigned to receive the diagnostic and those assigned to control. Adjusting for the trend in the retrospective time series at each site removed these differences.ConclusionsQuasi-experimental designs such as comparative interrupted time series can be used in diagnostic evaluation to estimate effect sizes before conducting a full randomised controlled trial or if a randomised trial is infeasible. In multi-site studies, existing retrospective data should be used to adjust for underlying differences between sites to make outcome data from different sites comparable, when possible.

Sampling rate-corrected analysis of irregularly sampled time series.

The analysis of irregularly sampled time series remains a challenging task requiring methods that account for continuous and abrupt changes of sampling resolution without introducing additional biases. The edit distance is an effective metric to quantitatively compare time series segments of unequal length by computing the cost of transforming one segment into the other. We show that transformation costs generally exhibit a nontrivial relationship with local sampling rate. If the sampling resolution undergoes strong variations, this effect impedes unbiased comparison between different time episodes. We study the impact of this effect on recurrence quantification analysis, a framework that is well suited for identifying regime shifts in nonlinear time series. A constrained randomization approach is put forward to correct for the biased recurrence quantification measures. This strategy involves the generation of a type of time series and time axis surrogates which we call sampling-rate-constrained (SRC) surrogates. We demonstrate the effectiveness of the proposed approach with a synthetic example and an irregularly sampled speleothem proxy record from Niue island in the central tropical Pacific. Application of the proposed correction scheme identifies a spurious transition that is solely imposed by an abrupt shift in sampling rate and uncovers periods of reduced seasonal rainfall predictability associated with enhanced El Niño-Southern Oscillation and tropical cyclone activity.

Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Estimators With Variation in Treatment Timing

This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates. An illustrative power analysis provides guidance on appropriate sample sizes. The key finding is that accounting for treatment timing increases required sample sizes. Further, DID estimators have considerably more power than standard CITS and ITS estimators. An available Shiny R dashboard performs the sample size calculations for the considered estimators.

Saved by the Bell Schedule? The Effects of a Later School Start Time on High Schoolers in an Urban District

Later school start times have emerged as a potential policy to improve the sleep and educational outcomes of teenagers. This study uses a quasi-experimental comparative interrupted time series approach to examine a 90-min delay in start times in an urban district in North Carolina. Results show that the later start time resulted in more sleep for students and improved course grades but limited evidence of benefits to other educational outcomes. In addition, the evidence suggests that disruptions from the change may temporarily decrease some educational outcomes, and that these disruption effects are concentrated among low-income students and students of color.

Swiss Job Market Monitor: A Rich Source of Demand-Side Micro Data of the Labour Market

Abstract The Swiss Job Market Monitor (SJMM) is a data collection of job ads for a national labour market from 1950 onwards. It is a prime example for demonstrating how to turn digital data into a high-quality social science data set. It is also exemplary for combining digital data with historically grown job ads data to provide a comparable time series covering the past 70 years. Paying close attention to sampling procedures, coverage, and key variables, the Data Brief shows how the SJMM profits from opportunities offered by computational social science and computational linguistics to navigate a wholly new set of challenges involved in creating such a data set. It closes with a discussion of new research opportunities this data set is opening for investigating the labour market as a core institution of modern society.

Comparison of time series and mechanistic models of vector-borne diseases

Vector-borne disease models are widely used to understand the dynamics involved in virus transmission. The simplest version of the mechanistic SEIR-SEI model is the most widely used representation of the dynamics involved in vector-borne diseases. Modifications to the basic model can improve the complex dynamics' acuracy. This work evaluates the capability of different models to represent the dynamics involved in dengue virus transmission. The models include a vector life stage representation, a re-susceptibility factor, and environmental variables in a mechanistic form.Furthermore, Autoregressive Integrated Moving Average methodologies (ARIMA method) were also used for comparison. The inclusion of environmental variables and vector life cycle improves the model's accuracy for mechanistic models, but the modification's complexity can restrict its applicability. Data-driven techniques were shown to be less accurate than all the mechanistic-based models (based on all criteria adopted).

Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

Interfamily grafting capacity of petunia.

In grafting, an agricultural technique for propagating flower species and fruit trees, two plants are combined to exploit their beneficial characteristics, such as rootstock disease tolerance and vigor. Grafting incompatibility has been observed, however, between distantly related plant combinations, which limits the availability of plant resources. A high grafting capacity has been found in Nicotiana, belonging to Solanaceae, but not in Ipomoea nil, a Convolvulaceae species. Here, we found that Petunia hybrida, another solanaceous species, has similar ability of interfamily grafting, which indicates that interfamily grafting capability in Solanaceae is not limited to the genus Nicotiana. RNA sequencing-based comparative time-series transcriptomic analyses of Nicotiana benthamiana, I. nil, and P. hybrida revealed that N. benthamiana and P. hybrida share a common gene expression pattern, with continued elevated expression of the β-1,4-glucanase subclade gene GH9B3 observed after interfamily grafting. During self-grafting, GH9B3 expression in each species was similarly elevated, thus suggesting that solanaceous plants have altered regulatory mechanisms for GH9B3 gene expression that allow tissue fusion even with other species. Finally, we tested the effect of the β-1,4-glucanase inhibitor D-glucono-1,5-lactone, using glucose as a control, on the interfamily grafting usability of P. hybrida with Arabidopsis rootstock. Strong inhibition of graft establishment was observed only with D-glucono-1,5-lactone, thus suggesting the important role of GH9B3 in P. hybrida grafting. The newly discovered grafting compatibility of Petunia with different families enhances the propagation techniques and the production of flower plants.

Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data From Geostationary Satellites (RTG)

Thermal infrared (TIR) land surface temperature (LST) products derived from geostationary satellites have a high temporal resolution in a diurnal cycle, but they have many missing values under cloudy-sky conditions. Therefore, it is pressing to obtain all-weather LST (AW LST) with a high temporal resolution by filling the gap of TIR LST. In this study, a method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly AW LST. Then, taking the Tibetan Plateau, which is a focus of climate change as a case, RTG was applied to the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST shows that the accuracy of the AW LST is better than the FY-4A LST and CLDAS LST under clear-sky, cloudy-sky, and all-weather conditions. The mean RMSEs are 3.02 K for clear-sky conditions, 3.94 K for cloudy-sky conditions, and 3.57 K for all-weather conditions. Uncertainty and coarse resolution of the original FY-4A and CLDAS data affect the accuracy of the obtained AW LST. The results of the LST time series comparison also show that the reconstructed AW LST is consistent with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST. The reconstructed AW LST also has good image quality and provides reliable spatial patterns. RTG is practical in obtaining high temporal resolution AW LST from the Chinese FY-4A to satisfy related applications. It can also be extended to other geostationary satellites and reanalysis datasets.

BUSINESS RISK IN TIMES OF ECONOMIC RECESSION AND ECONOMIC GROWTH

The issue of business risk in times of recession or growth is very topical in these times. The Czech Republic and its neighboring countries are currently struggling with the economic problems caused by the Covid-19 pandemic. The aim of the paper is to analyze the number of insolvency petitions in the Czech Republic from the years 2010–2020 and to compare them with each other. The data source are data from publicly available resources from the Creditreform group and also from the CRIF database of the Cribis platform. First were created tables for the Agriculture and Forestry, Manufacturing, Construction and Transport sectors. Data for the relevant years were added to them from the aforementioned resources. Then was created a line chart for each industry. Using time series analysis and comparison, we analyze and compare the development of insolvencies. At the same time, we use causal analysis to find out why there were high numbers of insolvencies in the given years. We are also looking at which sectors were hit hardest during the insolvency crisis, when they were hit hardest, how they did in times of economic growth and how they should adapt to the next possible crisis. During the Great Recession, the construction sector was hit the hardest, and then the transport sector also suffered greatly. The largest numbers of insolvencies can be observed in 2012. At a time of economic growth, which began in 2014, the numbers of insolvencies fell in all sectors analyzed, but mostly in agriculture and forestry and transport. Businesses in the sectors analyzed can adapt to the next crisis by creating larger cash reserves or changing, for example, crop production or transport by temporarily reducing fares to attract new customers. We see the benefit of this work in the analysis of the number of insolvencies in the given sectors, which has not been recorded in almost any academic papers. We also see a benefit in determining the conditions for how companies can prevent another crisis.