Temporal pattern and effect of COVID-19 on the trend of TB, DRTB, paediatrics TB and TB with HIV Coinfection: A decadal trend analysis by Pushpendra Bairwa, Mahesh C. Verma, Asha Kumari, Ajay Gupta, Yamini Singh

“…A multiplicative model was used for conducting time series analysis. The projected yearly number of cases were estimated using the line of best fit based on the least square method. …”
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Fractal-Based Robotic Trading Strategies Using Detrended Fluctuation Analysis and Fractional Derivatives: A Case Study in the Energy Market by Ekaterina Popovska, Galya Georgieva-Tsaneva

“…This paper presents an integrated robotic trading strategy developed for the day-ahead energy market that includes different methods for time series analysis and forecasting, such as Detrended Fluctuation Analysis (DFA), Rescaled Range Analysis (R/S analysis), fractional derivatives, Long Short-Term Memory (LSTM) Networks, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. …”
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A review of the determinants and prediction methods for off-channel water demand by HE Yanhu, XU Xiaodi

“…Additionally, hybrid approaches, combining time series analysis with other methods, address limitations in standalone models and enhance prediction accuracy. …”
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A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting by Ivanović Luka, Milić Saša D., Sokolović Živko, Rakić Aleksandar

“…In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set.…”
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How do events shape the media agenda on Islam and Muslims in Western Europe? An analysis of news events in Germany, the UK and France (2000–2020) by Liriam Sponholz, Anna-Maria Meuth

“…This database permitted a time series analysis to determine a) when the issues of Islam and Muslims received (no) attention and b) what events led to the “ups” or peaks in attention. …”
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Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction by Hamed Alizadegan, Behzad Rashidi Malki, Arian Radmehr, Hossein Karimi, Mohsen Asghari Ilani

“…This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. …”
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Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters by Jan Kleine Deters, Rasa Zalakeviciute, Mario Gonzalez, Yves Rybarczyk

“…The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. …”
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Deep Recurrent Model for Server Load and Performance Prediction in Data Center by Zheng Huang, Jiajun Peng, Huijuan Lian, Jie Guo, Weidong Qiu

“…Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. …”
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Renewable Energy Expansion in West Pomerania: Integrating Local Potential with Global Sustainability Goals by Jarosław Jaworski, Jakub Dowejko

“…Historical data from 2010 to 2023 were used to perform a time series analysis that evaluated the annual growth rate (AGR) of various RES technologies, including wind, solar, biomass, and biogas. …”
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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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RELEVANT ASPECTS REGARDING THE EUROPEAN UNION INDEPENDENCE ON ENERGY IMPORTS by ANGHEL MĂDĂLINA-GABRIELA, IACOB ȘTEFAN VIRGIL, STRIJEK DENIS-ARTHUR

“…In order to make a comparative analysis of the evolution / performances of energy systems in various countries, we applied the model based on time series analysis. These analyzes allow the exact position of a certain national energy system in relation to similar energy systems. …”
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Preventing suicide by restricting access to Highly Hazardous Pesticides (HHPs): A systematic review of international evidence since 2017. by Bruna Rubbo, Chao-Ying Tu, Lucy Barrass, Shu-Sen Chang, Flemming Konradsen, David Gunnell, Michael Eddleston, Chris Metcalfe, Duleeka Knipe

“…Only five studies assessed overall suicides; of those, four reported decreases in overall suicide rates following the intervention, of which three used time series analysis (range 7.0% to 45.1%). Only one study had a low risk of bias in all domains, with five studies having high risk of bias in at least one of the domains. …”
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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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Cloud and Cloud Shadow Detection for Multi-Modal Imagery With Gap-Filling Applications by Keunhoo Cho, Seongwook Park, Boram Seong, Seongwhan Lee, Jae-Pil Park

“…In conclusion, this approach not only proves beneficial for time-series analysis but also significantly reduces the time and effort required to build datasets in deep learning-based CCS detection.…”
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Riding Through the Pandemic: Unveiling Motorcycle Crash Trends Amidst Three Years of the COVID-19 Crisis by Shahrin Islam, Armana Sabiha Huq, Sadab Ishraq Khan, Sabah Hossain Iqra

“…The impacts of the pandemic on motorcycle-related road traffic crashes, injuries, and fatalities in Bangladesh are investigated in this study using ARIMA time series analysis. Data spanning 86 months (January 2016 to February 2023) were collected from the Accident Research Institute (ARI), which compiles newspaper-based data serving as an alternative source of information on crashes encompassing both pre-COVID (January 2016 to February 2020) and COVID-19 periods (March 2020 to February 2023). …”
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A Study on the Dependency Between Selected Global Stock Markets and Gold and Silver Futures by Ozan Kaymak

“…Specifically, the study seeks to analyze the potential cointegration and the effects of gold and silver futures returns on the returns of selected global stock markets using time-series analysis. The potential relationships between the monthly returns of selected global stock indices and the monthly returns of gold and silver futures were analyzed for the period from January 2014 to May 2024 using the Autoregressive Distributed Lag (ARDL) Bound Test method. …”
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On the Investigation of State Space Reconstruction of Nonlinear Aeroelastic Response Time Series by Flávio D. Marques, Eduardo M. Belo, Vilma A. Oliveira, José R. Rosolen, Andréia R. Simoni

“…Dynamic systems techniques based on time series analysis can be adequately applied to non-linear aeroelasticity. …”
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A case study on using a large language model to analyze continuous glucose monitoring data by Elizabeth Healey, Amelia Li Min Tan, Kristen L. Flint, Jessica L. Ruiz, Isaac Kohane

“…Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through data summarization and, more broadly, the potential to leverage LLMs for streamlined medical time series analysis.…”
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Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things by Banteng Liu, Wei Chen, Meng Han, Zhangquan Wang, Ping Sun, Xiaowen Lv, Jiaming Xu, Zegao Yin

“…Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.…”
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Predviđanje razvoja povrtarstva u Republici Srpskoj (Forecasting of Vegetable Production in Republic of Srpska) by Beba Mutavdžic, Ljiljana Drinic, Nebojša Novković, Aleksandar Ostojić, Gordana Rokvic

“…The prediction is based on modern quantitative methods, specifically applied the method of time series analysis , and used the appropriate ARIMA models.The form choice of the model is the result of qualitative analysis and statistical criteria. …”
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