Analysis of Rattleback Chaotic Oscillations by Michael Hanias, Stavros G. Stavrinides, Santo Banerjee

“…The emerging chaotic behaviour in all three cases has been studied and evaluated by the related time-series analysis and the calculation of the strange attractors’ invariant parameters.…”
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Synthesis of Vibration Waves Based on Wavelet Technology by L.H. Zou, A.P. Liu, X. Ma, C. Zhang, K. Huang

“…A novel method to generate time series of vibration waves is proposed in the paper. …”
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Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model by Cunjiang Xia, Yuyou Zhan, Yan Tan, Wenqing Wu

“…That is not only the first application of transformer models with a masked pretraining mechanism in time series forecasts but also taking the lead in exploring the possibility of this key system forecast. …”
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The law of the single price within one country: the example of Benin by N.C.C. Vikou

“…The study noted how a shock affecting prices in one region could also affect prices in other regions of the country, due to cross-dependence. The time series of consumer prices by regions of Benin were used. …”
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The Effects of Poverty on Health Outcomes in Nigeria: An ARDL Approach by Abdulrahman Abdulganiyu Idris

“…This study looks at the impact of poverty on people’s health in Nigeria. A time series data spanning 29 years, from 1991 to 2021 was used for the study. …”
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Adverse Environmental Impact: A Consultant’s Perspective by Alan W. Wells, Thomas L. Englert

“…In our over 30 years of experience, we have applied most of the approaches suggested for addressing this issue, including biostatistical methods, trend analysis, time series methods, conditional mortality rate models, stock-recruitment models, equivalent adult models, and ecosystem models. …”
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Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network by Longfei Wang, Hong Chen, Yang Li

“…We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. …”
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Empirical mode decomposition analysis of climate changes with special reference to rainfall data

“…<p>We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. …”
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Improving machine learning predictions to estimate fishing effort using vessel's tracking data by J. Samarão, A. Moreno, M.B. Gaspar, M.M. Rufino

“…Temporal resolution (ping rate) showed minimal effects on model performance, ranging from less than 2 % for intervals between 30 s (raw data with irregular time series) to 10 min (regular time series). As a post-processing step, it was concluded that replacing isolated data points with neighbouring values, significantly enhanced the detection of fishing events, with improvements ranging from 80 % to 250 %, depending on the model.In conclusion, this study presents a straightforward procedure for selecting a machine learning method and enhancing its power of classification using simple procedures. …”
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A population-based study on the burden of hospitalized pediatric pneumococcal disease in Taiwan before and after the introduction of 13-valent pneumococcal conjugate vaccine into t... by Ting-An Yen, Jhong-Lin Wu, Chi-Chuan Wang, Ling-Ya Huang, See-Hwee Yeo, Dony Patel, Cindy Thiow Koon Lim, Hung-Wei Lin, Eriko Yamada, Isaya Sukarom

“…Incidence time trends were assessed with interrupted time series analyses. Results 1,284 invasive pneumococcal disease episodes, 25,074 non-bacteremic pneumococcal pneumonia episodes, and 23,139 acute otitis media episodes were identified. …”
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Reconstructing the temperature degree of the warm seasons through Quercus persica tree rings in Zagros forests Dena region by Hossein zarean

“…After cross dating stage, to eliminate non-climate effects, May to September temperature average and tree rings time series were standardized. The Residual Chronology (RES) calculated by ARSTAN was calibrated with temperature degree of the period 1882-2011 and its positive and significant correlation with the width of growth rings was confirmed. …”
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Hybrid Model for Stock Market Volatility by Kofi Agyarko, Nana Kena Frempong, Eric Neebo Wiah

“…The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 100 and S&P 500). The outcome was then compared to the outcomes of the GARCH (1, 1), EGARCH (1, 1), GJR-GARCH (1, 1), and APARCH (1, 1) with different error distributions (ED) using the mean absolute percentage error (MAPE), the root mean square error (RMSE), Theil’s inequality coefficient (TIC) and QLIKE. …”
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Demand and consumption patterns of banana in Tanzania by William George

“…Cross-sectional data from 2021 and time series data from 2000 to 2021 were used. Dynamic econometric models, including the Constant Elasticity of Demand framework and log-linear regression, assess the impact of price, income, and consumer preferences on banana demand. …”
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Impact of antibiotics, iron oxide, and sodium sulfate on microbial community composition in laboratory-built municipal solid waste microcosms. by Judy Malas, Sarah C Khoury, Michael Tanzillo, Gracie A Fischer, Jean E Bogner, D'Arcy R Meyer-Dombard

“…To circumvent the challenge of MSW heterogeneity, we conducted a 65-day time series study on simulated MSW microcosms to assess microbiome changes using 16S rRNA sequencing in response to 1) Fe(OH)3 and 2) Na2SO4 to represent redox active components of C&D waste as well as 3) antibiotics. …”
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A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies by Yakun Wang, Jianglei Gong, Jie Zhang, Xiaodong Han

“…Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. …”
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Forecasting tourist in-flow in South East Asia: A case of Singapore by Manoj Kumar, Seema Sharma

“…Dickey Fuller, KPSS, HEGY, Ljung-Box, Box-Pierce etc.) were applied on the time series for adequacy of best model to fit, residual autocorrelation analysis and for the accuracy of the prediction.…”
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Spectrum Sensing in Very Low SNR Environment Using Multi-Scale Temporal Correlation Perception with Residual Attention by Song Hong, Weiqiang Xu

“…In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. …”
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Multidimensional Determinants of National Food Security in Azerbaijan: an Application of the ARDL Approach by Ragif Huseynov

“…The empirical analysis draws from nationally representative time series data over the period 1991 to 2018, taken from FAO and WDI. …”
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Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model by Shuang Zeng, Chang Liu, Heng Zhang, Baoqun Zhang, Yutong Zhao

“…This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. …”
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Refined Composite Multivariate Multiscale Fractional Fuzzy Entropy: Measuring the Dynamical Complexity of Multichannel Financial Data by Huiqin Chu, Zhiyong Wu, Wei Zhang

“…Refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE), which aims to sensitively discriminate different short noisy multichannel financial data, is proposed as a new measure to quantify the complexity dynamics of multichannel time series in this work. To better comprehend the RCmvMFFE measure, the dynamical complexity analyses of multichannel synthetic dataset are comparatively studied with multivariate multiscale fuzzy entropy (mvMFE), refined composite multivariate multiscale fuzzy entropy (RCmvMFE), and refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE). …”
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