A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City by Tanzina Akter, Md. Tanvirul Islam, Md. Farhad Hossain, Mohammad Safi Ullah

“…According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. …”
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Relationship between RSV-hospitalized children and meteorological factors: a time series analysis from 2017 to 2023 by Shuying Wang, Yifan Wang, Yingxue Zou, Cheng-liang Yin

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Determinants of Pro-Poor Growth and Its Impacts on Income Share: Evidence from Ethiopian Time Series Data by Gemechu Bekana Fufa

“…The dynamic ordinary least squares method was used to analyze the Ethiopian time series data from World Bank Development Indicators between 1990 and 2018 for the determinant of pro-poor growth. …”
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QTFN: A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series by Tao Chen, Yang Jiao, Lei Xie, Hongye Su

“…Nonstationary time series are ubiquitous in almost all natural and engineering systems. …”
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Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study by Lena Schmid, Moritz Roidl, Alice Kirchheim, Markus Pauly

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Automated Cattle Monitoring System for Calving Time Prediction Using Trajectory Data Embedded Time Series Analysis by Wai Hnin Eaindrar Mg, Thi Thi Zin, Pyke Tin, Masaru Aikawa, Kazayuki Honkawa, Yoichiro Horii

“…This research introduces an automated system for cattle monitoring and calving time prediction, utilizing trajectory data embedded with time-series analysis. Designed for large-scale farms, our system offers continuous 12-h monitoring, ensuring precise capture of cattle movements. …”
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Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching by Mingyue Tan, Jiming Li, Xiangqian Chen, Xuezhen Cheng

“…To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. …”
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Artificial intelligence for biodiversity: Exploring the potential of recurrent neural networks in forecasting arthropod dynamics based on time series by Sébastien Lhoumeau, João Pinelo, Paulo A.V. Borges

“…This research conducted a comparative analysis of Local Polynomial Regression (LOESS), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Recurrent Neural Network (RNN) models for time-series prediction. Using a unique Long-Term Monitoring Program for island forest arthropods (2012–2023), wherein we selected the 39 most prevalent species collected using SLAM (Sea Land Air Malaise) traps within a native forest fragment on Terceira Island in the Azores archipelago. …”
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Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints by Mei Wang, Xinyuan Zhu, Guangyue Zhou, Kewen Li, Qingshan Wu, Wankai Fan

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Navigating the current landscape of ocean observations: an overview from platform infrastructures to networks related to ocean time series by Anabel von Jackowski

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Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study by Wei Wu, Zheng-gang Fang, Shu-qin Yang, Cai-xia Lv, Shu-yi An

“…A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.Design Time-series study.Setting The USA was the setting for this study.Main outcome measures Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.Results In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.Conclusions The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.…”
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Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis by Mohanaad Shakir

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Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Abdelmgeid A. Ali, Abdu Gumaei, Mabrook Al-Rakhami

“…Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. …”
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Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures by Minshui Huang, Wei Zhao, Jianfeng Gu, Yongzhi Lei

“…This study presents a methodology incorporating the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. …”
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A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting by Jiang Wu, Tengfei Zhou, Taiyong Li

“…The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. …”
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Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units by Peifeng Ma, Zeyu Jiao, Zherong Wu

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Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm by Liping Yang, Chunxia Wang, Pan Zhou, Na Xie, Maozai Tian, Kai Wang

“…This study employed the BEAST algorithm to decompose the brucellosis time series in Xinjiang from 2010 to 2023, while simultaneously identifying change points in the decomposed seasonal and trend components. …”
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Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves by Sameera Abdulsalam Othman, Haithem Taha Mohammed Ali

“…In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. …”
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Remote sensing-derived time series of transient glacier snowline altitudes for High Mountain Asia, 1985–2021 by David Loibl, Niklas Richter, Inge Grünberg

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Evaluating programmatic reactive focal drug administration impact on malaria incidence in northern Senegal: an interrupted time series analysis by Ellen Leah Ferriss, Yakou Dieye, Moustapha Cissé, Gnagna Dieng Sow, Jean Louis Lankia, Damien Diedhiou, Abiboulaye Sall, Tamba Souane, Tidiane Thiam, Doudou Sene, Elhadji Doucouré, Ibrahima Diallo, Adam Bennett, Caterina Guinovart

“…Methods An interrupted time series analysis was conducted with routine surveillance data on health post-level monthly confirmed malaria case counts from the District Health Information Software (DHIS2). …”
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