Use of ARIMA Model for Forecasting Consequences Due to Traffic Crashes in the Kingdom of Saudi Arabia by Saleh Al Sulaie

“…Times series models are important statistical methods for analysing data recorded at points of time which considers the order of observations. …”
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Livestock and poultry manure pollution and its future risk forecasting on farmland in Henan Province by WU Rongkang, PAN Fanghui, CHANG Jiayue, ZHANG Xing, TIBAMBA Matthew, WANG Youbao

“…It was analysed comprehensively for farmland pollution by the livestock and poultry manure in Henan Province from 2008 to 2017 and forecasting its future risks, which providing a theoretical basis for Henan Province to arrange rationally the structure of the livestock and poultry breeding industry and carry out the work of pollution control. …”
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Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting by Daben Yu, Zongping Li, Qinglun Zhong, Yi Ai, Wei Chen

“…We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. …”
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A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting by Huawei Zhai, Ruijie Tian, Licheng Cui, Xiaowei Xu, Weishi Zhang

“…Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. …”
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Remote sensing of Andean mountain snow cover to forecast water discharge of Cuyo rivers by Nicolas Delbart, Samuel Dunesme, Emilie Lavie, Malika Madelin, Régis Goma

“…Our research indicates that it is possible to use remote sensing to forecast the average discharge in the September-April period (high-water season) from MOD10A2 images with an average uncertainty of 15%. …”
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Time series modelling and forecasting of Monkeypox outbreak trends Africa's in most affected countries by Diptismita Jena, Sathvik Belagodu Sridhar, Javedh Shareef, Sirajunisa Talath, Suhas Ballal, Sanjay Kumar, Mahakshit Bhat, Shilpa Sharma, M Ravi Kumar, Ashish Singh Chauhan, Abhay M. Gaidhane, Neha Agarwal, Ganesh Bushi, Muhammed Shabil, Quazi Syed Zahiruddin, Aroop Mohanty, Jaffar A. Al-Tawfiq, Ranjit Sah

“…Time series analysis techniques, including ARIMA models and Join Point Regression, were employed to forecast Mpox trends and analyse the annual percentage change in new cases. …”
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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…Precipitation did not display consistent patterns or a high relevance predictor for most of the stations analysed.</p> <p>The random forest classifier is not required to have considerable complexity, and the computing time to issue predictions is typically a few minutes on a personal laptop. …”
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Iterative Forecasting of Financial Time Series: The Greek Stock Market from 2019 to 2024 by Evangelos Bakalis, Francesco Zerbetto

“…We employ an iterative stochastic differential equation that accurately forecasts an economic time series’s next value by analysing its past. …”
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Analysing COVID-19 Verified, Recuperate and Death Cases in Ethiopia Using ARIMA Models by Birhanu Betela Warssamo

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Analysing COVID-19 Verified, Recuperate and Death Cases in Ethiopia Using ARIMA Models by Birhanu Betela Warssamo

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Forecast of balance of a manpower as way of management of an environment of a youth segment of labor market by N. G. Vishnevskaya

“…The author carried out the analyses of deficient and over professions on the basis of the amount of tension ratio on separate professions of workers and of fice employees.…”
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SELF-SUFFICIENCY OF THE TURKISH AQUACULTURE SECTOR AND FORECASTING ITS PRODUCTION WITH THE ARIMA (BOX-JENKINS) MODEL by Asli DALGIC, Deniz SARICA, Vecdi DEMIRCAN

“…The amount of aquaculture production was estimated with the Box-Jenkins ARIMA method. As a result of the analyses performed, the ARIMA (1, 2, 2) model was found to be the most suitable model for forecasting aquaculture production. …”
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BREATHE: A GeoAI-Powered Air Quality Monitoring and Forecasting System for Urban Sustainability by S. D. Adduri, T. Ali

“…BREATHE system features four essential aspects that include (1) real-time AQI checks at 12 locations across the UAE territory (2) predictive models for AQI forecasting through climatic and historical data analyses (3) interactive dashboards with mapping visuals and alert features and (4) AI-powered chatbot assistance along with non-specialist user-friendly accessibility. …”
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Segmenting Online Shoppers: A Combined Cluster and Logistic Regression Approach for Forecasting Purchase Behavior by Wong Ming Wong, Wunhong Su

“…There are two research questions for the paper: 1) How can marketers segment prior customers&#x2019; online shopping behavior to develop a more accurate consumer segmentation for forecasting purchase behavior? 2) Which factors influence customers&#x2019; online shopping behavior? …”
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Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design by Yifeng Wang, Ping Wang, Zihan Li, Zhengxing Chen, Qing He

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Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva, Lourenço Lázaro Magaia

“…This study aimed to systematize the parametric forecast (PF) of solar energy over time, adopting the validation of estimates by machine learning models (MLMs), with highly complex analyses as inclusion criteria and studies not validated in the short or long term as exclusion criteria. …”
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Uncertainty-Aware Earthquake Forecasting Using a Bayesian Neural Network with Elastic Weight Consolidation by Changchun Liu, Yuting Li, Huijuan Gao, Lin Feng, Xinqian Wu

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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…This study addresses key research gaps in wind energy forecasting, including the inability of traditional statistical models to capture complex, nonlinear temporal patterns, the underutilization of real-time, location-specific data, the lack of comparative analyses across diverse models and datasets, and the absence of systematic model selection strategies for future forecasting. …”
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Mixed-frequency VAR: a new approach to forecasting migration in Europe using macroeconomic data by Emily R. Barker, Jakub Bijak

“…Forecasting international migration is a challenge that, despite its political and policy salience, has seen a limited success so far. …”
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Short-term spatial and temporal forecast of dry snow avalanches of sublimation recrystallization and mixed origin by Yu. B. Andreev, A. N. Bozinsky, P. A. Chernous

“…But in comparison with purely dry and wet fresh snow avalanches forecast analysed before the examined here above types are less precisely predicted. …”
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