Spatiotemporal Variation and Abrupt Change Analysis of Temperature from 1960 to 2012 in the Huang-Huai-Hai Plain, China by Yanyu Yin, Hui Liu, Xiangsheng Yi, Weidong Liu

“…Based on a monthly dataset of temperature time series (1960–2012) in the Huang-Huai-Hai Plain of China (HHHPC), spatiotemporal variation and abrupt change analysis of temperature were examined by moving average, linear regression, spline interpolation, Mann-Kendall test, and moving t-test. …”
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Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model by Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Joachim Reuder, Michel d. S. Mesquita, Sulin Tao, Triphonia J. Ngailo

“…The graphical tools (such as time series and cumulative curves) show the performance of the model with time. …”
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A fuzzy compromise solution framework for assessing the early effect of COVID-19 on global stock indices by Sanjib Biswas

“…To this end, in this paper, the researchers put forth a fuzzy entropy (for determining criteria weights) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methodology utilizing time series data for the performance analysis of global indices during July 01, 2019, to May 31, 2020, distributed in two phases such as phase 1: July 01, 2019, to December 31, 2019 (pre-crisis) and phase 2: January 01, 2020, to May 31, 2020 (crisis). …”
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Comparative Study of Metaheuristics for the Curve-Fitting Problem: Modeling Neurotransmitter Diffusion and Synaptic Receptor Activation by Jesús Montes, Antonio LaTorre, Santiago Muelas, Ángel Merchán-Pérez, José M. Peña

“…Simulations, however, do not provide generalized models of the underlying biochemical phenomena, but a set of observations, or time-series curves, displaying the behavior of the synapse in the scenario represented. …”
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Spatiotemporal Variability in Wind Turbine Blade Leading Edge Erosion by Sara C. Pryor, Jacob J. Coburn, Rebecca J. Barthelmie

“…Results indicate blade coating failure at some sites in as few as 4 years, and that the frequency and intensity of material stresses are both highly episodic and spatially varying. Time series analyses indicate that up to one-third of blade coating lifetime is exhausted in just 360 5-min periods in the Southern Great Plains (SGP). …”
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Adaptive CNN Ensemble for Complex Multispectral Image Analysis by Syed Muslim Jameel, Manzoor Ahmed Hashmani, Mobashar Rehman, Arif Budiman

“…Due to the complexities (spatial, spectral, dynamic data sources, and temporal inconsistencies) in online and time-series multispectral image analysis, there is a high occurrence probability in variations of spectral bands from an input stream, which deteriorates the classification performance (in terms of accuracy) or makes them ineffective. …”
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Comparing the Effect of Public and Private Health Expenditures on the Health Status of D-8 Countries by Sepideh Arab, Ebrahim Ghaed, Atefeh Mazinani

“…Purpose: This study uses the Panel Data model to compare the effect of public and private health expenditures on the health status of D-8 member countries from 2020 to 1995.Methodology: The statistical population studied in this research includes research from D-8 group member countries. Time series information about these countries was collected from reputable international sources, including the World Bank, which, using Eviews software, was tested. …”
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Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm by Li Yu Yab, Noorhaniza Wahid, Rahayu A Hamid

“…The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. …”
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DYNAMIC INTERACTIONS AMONG EXCHANGE RATE, PERFORMANCE OF MANUFACTURING SECTOR AND INFLATION IN NIGERIA: AN ARDL APPROACH by Adedayo O. ADEDEJI, Oyeyemi O. ADEBIYI, Barakat I. SHOBANDE

“…Secondary and annual time series data that covered 1981 to 2018 are sourced from World Development Indicator (WDI) published by World Bank Organization. …”
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Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods by Seungjoo Lee, Yongjin Kim, Bongjun Ji, Yongseong Kim

“…This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. …”
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Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach by Zeyue Li, Jianzhao Bi, Yang Liu, Xuefei Hu

“…Existing methods, including chemical transport models (CTMs) and time series at air monitoring sites, forecast O3 and NO2 concentrations either with nontrivial uncertainty or without spatiotemporally continuous coverage. …”
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Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output by Junyi Shen, Tetsuro Miyazaki, Swaninda Ghosh, Toshihiro Kawase, Kenji Kawashima

“…Experimental evaluations indicate that the SEPRC model outperforms traditional PRC in clustering time‐series reservoir output sequences with the same dimensionality. …”
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Predictive Analytics of In-Service Bridge Structural Performance from SHM Data Mining Perspective: A Case Study by Qiwen Jin, Zheng Liu, Junchi Bin, Weixin Ren

“…The data mining methods proposed (distribution function, association analysis, and time-series analysis) are employed for the analysis and prediction of structural response and deterioration extent. …”
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A chaotic bursting-spiking transition in a pancreatic beta-cells system: observation of an interior glucose-induced crisis by Jorge Duarte, Cristina Januário, Nuno Martins

“…We provide a description of the crisis mechanism at the bursting-spiking transition point in terms of time series variations and based on certain amplitudes of invariant intervals associated with return maps. …”
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Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model by Gopi Battineni, Nalini Chintalapudi, Francesco Amenta

“…Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data. …”
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Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition by YuKang Jia, Zhicheng Wu, Yanyan Xu, Dengfeng Ke, Kaile Su

“…Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recogniton and image recognition. …”
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Exploring Interconnections Among Environmental Degradation, Energy Consumption, Information and Communication Technology, and Total Factor Productivity in Iran by M. Abbasian

“…This descriptive-analytical and applied study used time series data from the World Bank and Iran Statistics Center from 2000 to 2023. …”
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Comparing the Impact of Renewable and Non-Renewable Energy Consumption on the Economic Welfare of Iranian Provinces Using a Panel Data Approach by Mahmoud Rahimi, Mohammad Mirbagherijam, Ebrahim Ghaed, Akram Noorani

“…The statistical population includes research on the provinces of Iran. Time series information about these provinces has been collected from data available in the Energy Balance, Statistics Center, and Iran Economic and Financial Database, which was tested using Eviews software. …”
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A Multi-Scale Convolutional Residual Time-Frequency Calibration Method for Low-Accuracy Air Pollution Data by Jiahao Liu, Fei Shi, Zhenhong Jia, Jiwei Qin

“…The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. …”
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Real-time prediction of port water levels based on EMD-PSO-RBFNN by Lijun Wang, Shenghao Liao, Sisi Wang, Jianchuan Yin, Ronghui Li, Jingyu Guan

“…First, through the application of EMD, the port water level time series was decomposed into sub-series characterized by lower non-linearity. …”
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