Küreselleşmenin Yoksulluk Üzerine Etkisi: Türkiye Örneği by Başak Karşıyakalı, Gökhan Ersoy

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Adaptive algorithms for change point detection in financial time series by Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov

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ÜNİVERSİTELERİN YEREL GELİR VE İSTİHDAM ÜZERİNDEKİ ETKİLERİ: BATMAN ÜNİVERSİTESİ ÜZERİNE BİR UYGULAMA by Mücahit Çayın, Sevda Yapraklı

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ÇOKLU YAPISAL KIRILMALAR ALTINDA ENFLASYON İLE ULUSLARARASI PETROL FİYATLARI İLİŞKİSİ: TÜRKİYE ÖRNEĞİ by Gökhan Kartal

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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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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain by Zeynep Hilal Kilimci, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, Mehmet Ali Ekmis

“…For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. …”
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Astronomical Chronology Framework of the Lingshui Formation (Oligocene) in the Northern South China Sea by Jianhao Liang, Yaning Wang, Shangfeng Zhang, Yubing Liang, Gaoyang Gong, Rui Han

“…The astronomical timescale framework for the Lingshui Formation was accurately constructed by integrating geophysical logging data and employing a multidisciplinary approach that includes time series analysis, cyclostratigraphy, astronomical dating, and Power Ratio Accumulation (PRA) methods. …”
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Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques by Tanzina Akter, Md. Farhad Hossain, Mohammad Safi Ullah, Rabeya Akter

“…To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. …”
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A New Method of Kronecker Product Decomposition by Yi Wu

“…Kronecker product decomposition is often applied in various fields such as particle physics, signal processing, image processing, semidefinite programming, quantum computing, and matrix time series analysis. In the paper, a new method of Kronecker product decomposition is proposed. …”
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Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis by Juanjuan Shi

“…This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. …”
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Networks of care for optimizing Primary Health Care Service Delivery in Ethiopia: Enhancing relational linkages and care coordination. by Gizachew Tadele Tiruneh, Nebreed Fesseha, Temesgen Ayehu, Tamar Chitashvili, Mesele Damte Argaw, Biruhtesfa Bekele Shiferaw, Mikias Teferi, Agumasie Semahegn, Biruk Bogale, Yibeltal Kifle, Hillina Tadesse, Chala Tesfaye, Dessalew Emaway

“…Service statistics were analyzed using Student's t-test and interrupted time-series analysis to compare maternal and child care outcomes before and after the NoCs intervention, with counterfactual estimates generated to assess the intervention's impact. …”
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A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features by Huansan Zhao, Chunyan Chang, Zhuoran Wang, Gengxing Zhao

“…Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). …”
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Understanding Human Papillomavirus Vaccination Hesitancy in Japan Using Social Media: Content Analysis by Junyu Liu, Qian Niu, Momoko Nagai-Tanima, Tomoki Aoyama

“…Natural language processing techniques and large language models (LLMs) were used for stance analysis of the collected data. Time series analysis and latent Dirichlet allocation topic modeling were used to identify shifts in public sentiment and topic trends over the decade. …”
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A Semiparametric Approach for Modeling Partially Linear Autoregressive Model with Skew Normal Innovations by Leila Sakhabakhsh, Rahman Farnoosh, Afshin Fallah, Mohammadhassan Behzadi

“…The nonlinear autoregressive models under normal innovations are commonly used for nonlinear time series analysis in various fields. However, using this class of models for modeling skewed data leads to unreliable results due to the disability of these models for modeling skewness. …”
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The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction by Kejia He, Hongyu Cheng, Yuchen Zhou, Cuihua Xie

“…In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. …”
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Identification of core genes related to exosomes and screening of potential targets in periodontitis using transcriptome profiling at the single-cell level by Wufanbieke Baheti, Diwen Dong, Congcong Li, Xiaotao Chen

“…B cells and T cells holding crucial parts in PD were identified as key cell types. Pseudo-time series analysis revealed that the expression of IGLL5 and MZB1 increased during T cell differentiation, increased and then decreased during B cell differentiation. …”
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Time and Space View of Human-Water Relationship Discipline and Its Research Methods by ZUO Qiting

“…The research object of the human-water relationship discipline is the human-water system composed of nature and human society.It has distinct time and space properties and will inevitably encounter problems of temporal and spatial scales.Therefore,it is of great significance to study and clarify the time and space view of the human-water relationship discipline.On the basis of the previous research and common examples,this paper expounds the temporal and spatial scales of research on the human-water relationship.Then,we put forward the time and space view of the human-water relationship discipline and discuss its main arguments and application approaches.After that,we introduce the research methods of the spatiotemporal analysis of the human-water relationship, which mainly include three categories,i.e.,the time series analysis method,spatial evolution analysis method,and spatiotemporal coupling analysis method.Finally,the prospect of further research is given.The research can lay a foundation for the human-water relationship discipline to deal with problems of temporal and spatial scales and can also provide a reference for research on similar scale problems.…”
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A comparative study of symbolic aggregate approximation and topological data analysis by Fredrik Hobbelhagen, Ioannis Diamantis

“…Specifically, we applied topological data analysis (TDA) to analyze stock time series data and compared the results with those obtained using an expanded form of a more classical time series analysis method, symbolic aggregate approximation (SAX). …”
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Surveillance des effets de la pollution atmosphérique en milieu urbain sur la santé : le Programme français PSAS-9 by Sylvie Cassadou, Christophe Declercq, Daniel Eilstein, Pascal Fabre, Laurent Filleul, Alain Le Tertre, Agnès Lefranc, Sylvia Medina, Catherine Nunes, Laurence Pascal, Hélène Prouvost, Abdelkrim Zeghnoun

“…These data were modelized according to the time series analysis method. Depending on the pollutant, excess in total mortality risk related to a 10 µg/m3 pollutant increase varied between 1.2 and 1.9%. …”
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Unemployment-Inflation Relationship in Germany by Abdullah Miraç Bükey, Murat Kalkan

“…The empirical study method is time series analysis, and the monthly data set covers the period from January 1992 to April 2023. …”
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