Insights on Climate-Driven Fluctuations of Cave 222Rn and CO2 Concentrations Using Statistical and Wavelet Analyses by Concepcion Pla, Angel Fernandez-Cortes, Soledad Cuezva, Juan Jose Galiana-Merino, Juan Carlos Cañaveras, Sergio Sanchez-Moral, David Benavente

“…For this purpose, we combined a set of mathematical methods that included a statistical and wavelet analysis of a 6-year time series in Rull Cave (Spain). Generally, the 222Rn and CO2 dynamic in cave air showed similar patterns. …”
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Wearable Power Assistant Robot Sensor Signal Prediction Algorithm and Controller Design by Gang Li, Guoheng Ren

“…In order to improve the dynamic response frequency of the wearable robotic perception system, a sensor signal based on time series analysis is proposed. The online prediction algorithm, which can perform single-step or multistep prediction under the premise of ensuring certain accuracy, can multiply the dynamic response frequency of the wearable-assisted robot sensing system to ensure the real-time performance of the whole system. …”
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D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term Memory by Linliang Zhang, Shuyun Xu, Shuo Li, Lihu Pan, Su Gong

“…The Conv-LSTM processes the approximation coefficients to capture the long-term characteristics of the time series, while the multiple layers of the MGDCN process the detail coefficients to capture short-term fluctuations. …”
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Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques by Soojun Kim, Huiseong Noh, Narae Kang, Keonhaeng Lee, Yonsoo Kim, Sanghun Lim, Dong Ryul Lee, Hung Soo Kim

“…The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). …”
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The Diagnostic and Predictive Significance of Immune-Related Genes and Immune Characteristics in the Occurrence and Progression of IgA Nephropathy by Jian-Bo Qing, Wen-Zhu Song, Chang-Qun Li, Ya-Feng Li

“…GSE116626 was used as a training set to identify different immune-related genes (DIRGs) and establish machine learning models for the diagnosis of IgAN; then, a nomogram model was generated based on GSE116626, and GSE115857 was used as a test set to evaluate its clinical value. Short Time-Series Expression Miner (STEM) analysis was also performed to explore the changing trend of DIRGs with the progression of IgAN lesions. …”
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Comprehensive Environmental Monitoring System for Industrial and Mining Enterprises Using Multimodal Deep Learning and CLIP Model by Shuqin Wang, Na Cheng, Yan Hu

“…Finally, a multimodal LSTM model is leveraged to detect anomalies in the monitoring data by capturing long-term dependencies within time series information. The model was trained and evaluated using real-time data from a coal mining enterprise’s environmental monitoring system spanning March to September 2023. …”
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Analysing COVID-19 Verified, Recuperate and Death Cases in Ethiopia Using ARIMA Models by Birhanu Betela Warssamo

“… Applying a successful prediction of the confirmed, recovered and deaths is thought to be the basic requirement to successfully control the spreading rate of diseases. Time series models have extensively been considered as the suitable methods to forecast the confirmed, recovered and deaths because of the virus. …”
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Balancing Progress and Preservation: The Complex Interplay of Economic Growth and Forest Conservation in Nepal’s Carbon Dioxide Emissions by Omkar Poudel, Pradeep Acharya, Sarad Chandra Kafle, Basanta Prasad Adhikari

“…The analysis utilized time series data from 1990 to 2020, employing the dynamic ordinary least squares (DOLS) method. …”
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Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM by Yaping Wang, Chaonan Yang, Di Xu, Jianghua Ge, Wei Cui

“…Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. …”
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A comprehensive spatiotemporal and risk reduction drought assessment study utilizing SPEI index for Urmia Lake Basin, Iran by Sadaf Samiei, Mohammadali Alijanian

“…Using a 50-year (1971–2020) time series of multivariate Standardized Precipitation-Evapotranspiration Index (SPEI) data (3, 6, 9, and 12-month timescales), the study employs Mann-Kendall, modified Mann-Kendall, and Sen's slope analyses to determine drought trends, examines spatial patterns using Severity Area Frequency (SAF) curves, and finally, evaluates drought risk reduction using resilience, vulnerability, and exposure factors. …”
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Diabetic Gait Is Not Just Slow Gait: Gait Compensations in Diabetic Neuropathy by Adrienne D. Henderson, A. Wayne Johnson, Sarah T. Ridge, Jonathan S. Egbert, Kevin P. Curtis, Levi J. Berry, Dustin A. Bruening

“…A full-body model with a multisegment foot was used to calculate inverse dynamics and analyze sagittal plane metrics and time series waveforms across stance phase. Results. …”
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PRKD2 as a novel target for targeting the diabetes–osteoporosis nexus by Rongjin Chen, Chenhui Yang, Hefang Xiao, Ao Yang, Changshun Chen, Fei Yang, Bo Peng, Bin Geng, Yayi Xia

“…By employing the Mfuzz time-series gene clustering method combined with transcriptome sequencing of patient serum, we systematically delineated gene expression patterns during the transition from a healthy state through DM to DMOP. …”
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Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers by Mohammed Achite, Okan Mert Katipoğlu, Veysi Kartal, Metin Sarıgöl, Muhammad Jehanzaib, Enes Gül

“…In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. …”
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A New Method of Evaluating the Symmetry of Movement Used to Assess the Gait of Patients after Unilateral Total Hip Replacement by Slawomir Winiarski, Alicja Rutkowska-Kucharska, Andrzej Pozowski, Krzysztof Aleksandrowicz

“…The symmetry function is a simple method which can complement other robust methods in time series data evaluation and interpretation.…”
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Modeling of investment attractiveness of countries using entropy analysis of regional stock markets by H. Danylchuk, N. Chebanova, N. Reznik, Y. Vitkovskyi

“…We have calculated the permutation entropy for the time series of stock markets of countries for the period from 2005 to 2018. …”
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Temporal trends of the incidence rate of syphilis during pregnancy and congenital syphilis in São Paulo, Brazil, 2011-2023 by Beatriz Poddis Busquim e Silva, Fernanda Penido Matozinhos, Rafaela Siqueira Costa Schreck, Fernanda Marçal Ferreira, Camilla Pontes Bezerra, Bárbara Tideman Sartório Camargo, Thales Philipe Rodrigues da Silva

“…Methods Ecological time series study, based on data from Notifiable Health Conditions Information System records. …”
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A model of competing saturable kinetic processes with application to thepharmacokinetics of the anticancer drug paclitaxel by Rebeccah E. Marsh, Jack A. Tuszyński, Michael Sawyer, Kenneth J. E. Vos

“…We present and classify the results of time series for the drug concentration in the body to uncover the underlying power laws. …”
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Uncertainties of the rainfall regime in a tropical semi-arid region: the case of the State of Ceará by Eunice Maia de Andrade, Maria Gina Torres Sena, Antonio Givanilson Rodrigues da Silva, Francisco Jairo Soares Pereira, Fernando Bezerra Lopes

“…The study considered time series of least 30 years, from 77 rain gauge stations around the state of Ceará. …”
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Transfer learning for non-image data in clinical research: A scoping review. by Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman

“…Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). …”
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Iraqi Stock Market Prediction Using Artificial Neural Network and Long Short-Term Memory by Sama Hayder Abdulhussein AlHakeem, Nashaat Jasim Al-Anber, Hayfaa Abdulzahra Atee, Mahmod Muhamad Amrir

“…The choice of the proper model of time series data affects the precision of the predictions, and stock market data is typically random and turbulent for various industries. …”
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