“…The continuously expanding toolbox of nonlinear time series analysis techniques has recently highlighted the importance of dynamical complexity to understand the behavior of the complex solar wind–magnetosphere–ionosphere–thermosphere coupling system and its components. …”
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“…The combination of network sciences, nonlinear dynamics and time series analysis provides novel insights and analogies between the different approaches to complex systems. …”
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“…As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.…”
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“…Therefore, in this paper, we propose a method to analyze asymmetric walking using Dynamic Time Warping (DTW) distance, a time series analysis method. The DTW distance was obtained by combining gyroscope data and pressure data. …”
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“…We develop an innovative experimental approach, integrating biologically inspired robotics, time-series analysis, and computer vision, to build a detailed profile of the effects of non-lethal stress on the ecology and evolution of mosquitofish (Gambusia holbrooki)—a global pest. …”
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“…Both supervised machine learning and time series analysis were employed to analyze 350,059 Weibo posts released by 3,883 news sources between December 2019 and April 2020. …”
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“…Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. …”
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“…Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. …”
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“…We assessed the effect of this one-week lockdown, coupled with mass testing, on reducing the daily number of new confirmed cases and asymptomatic cases during the Omicron wave, using an interrupted time series analysis approach. Our analysis suggests that the one-week citywide lockdown in Shenzhen was effective at lowering both daily new confirmed cases and asymptomatic cases during the Omicron wave. …”
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“…This paper introduces the potential outcomes framework for causal inference and summarizes well‐established causal analysis methods for observational data, including matching, inverse probability treatment weighting, the instrumental variable method and interrupted time‐series analysis with controls. It provides examples in addiction research and guidance and analysis codes for conducting these analyses with example data sets.…”
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“…In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. …”
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“…We calculated the incidence rates (per 1000 person-years) of cystitis, pyelonephritis, and lab-based urine tests per age (< 2, 2-4, 5-9 and 10-18 years)) and gender in children and performed an autoregressive time-series analysis and seasonality analysis. In children with UTI, we calculated the number of lab-based urine tests and antibiotic prescriptions per person-year and performed an autoregressive time-series analysis. …”
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“…The interrupted time-series analysis showed a downward level change in ICU bed occupancy during the COVID-19 pandemic (− 4.29%, 95% confidence intervals − 5.69 to − 2.88%), and HDU bed occupancy showed similar trends. …”
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“…According to the interrupted time series analysis, the total antibiotic consumption decreased significantly immediately after the COVID-19 pandemic outbreak (coef. = − 2.712, p = 0.045), but it then increased significantly over a long-term (coef. = 0.205, p = 0.005). …”
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“…Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. …”
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“…These include the geometry of preferred orientations of contours and edge effects on maps, time series analysis of Quaternary retreating ice sheet related sedimentary data, estimation of first and last appearances of fossil taxa from frequency distributions of their observed first and last occurrences, tectonic reactivation along pre-existing schistosity planes in fold belts, use of the grouped jackknife method for bias reduction in geometrical extrapolations, and new applications of the theory of permanent, volume-independent frequency distributions.…”
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“…This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. …”
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“…The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. …”
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“…In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. …”
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“…Time series arise from the abundance of sequential data in various fields, and time series analysis uncovers patterns, trends, and dependencies within the temporal data, enabling informed decision-making and predictions for a wide range of applications. …”
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