The burden of attention deficit hyperactivity disorder and incidence rate forecast in China from 1990 to 2021 by Ningyu Li, Ningyu Li, Junqiang Zhao, Junqiang Zhao, Junqiang Zhao, Junqiang Zhao, Fujun Zhou

“…Age-period-cohort (APC) modeling was applied to disentangle the effects of age, calendar period, and birth cohort on disease burden. …”
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Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives by Weihe Zhu, Daxi Wang, Pengsong Li, Haohao Deng, Ziqing Deng

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Flare Set-Prediction Transformer: A Transformer-Based Set-Prediction Model for Detailed Solar Flare Forecasting by Liang Qiao, Gang Qin

“…This work presents the set-prediction framework and the FSPT model, showing its potential for more informative flare forecasting.…”
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Forecasting Urban Sprawl Dynamics in Islamabad: A Neural Network Approach by Saddam Sarwar, Hafiz Usman Ahmed Khan, Falin Wu, Sarah Hasan, Muhammad Zohaib, Mahzabin Abbasi, Tianyang Hu

“…Utilizing a land change modeler (LCM), forecasts of the future conditions in 2025, 2030, and 2035 are predicted. …”
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Analytical Methods for Assessing and Forecasting Financial Standing of Credit Institutions by Y. M. Beketnova

“…The objective of the article is to propose a new approach to assessing and forecasting fnancial condition of credit institutions and to early detection of those that have high risks of license revocation. …”
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Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study by Xin Xiong, Linghui Xiang, Litao Chang, Irene XY Wu, Shuzhen Deng

“…The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. …”
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Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors by Ioan Susnea, Emilia Pecheanu, Adina Cocu, Adrian Istrate, Catalin Anghel, Paul Iacobescu

“…Additionally, the approach successfully detected simulated gradual declines in mobility (1% per day reduction), evidenced by statistically significant regression trends in activity levels over time. (4) Conclusions: The study argues that non-intrusive binary sensors, combined with lightweight forecasting models and fuzzy inference, may provide a practical and scalable solution for detecting mobility anomalies in older adults. …”
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A transferable machine learning model for real-time forecast of epidemic dynamics and pre-trigger event warning by Enpei Chen, Xiong Yu

“…The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.…”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. A comprehensive review of literature has been conducted by examining over 400 articles to focus on 72 studies including 14 reviews publications on ML and DL models about potato disease forecasting using different techniques. …”
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Enhancing Smart Grids for Sustainable Energy Transition and Emission Reduction with Advanced Forecasting Techniques by Farah Rania, Farou Brahim, Kouahla Zineddine and Seridi Hamid

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Multimodal Computational Approach for Forecasting Cardiovascular Aging Based on Immune and Clinical–Biochemical Parameters by Madina Suleimenova, Kuat Abzaliyev, Ainur Manapova, Madina Mansurova, Symbat Abzaliyeva, Saule Doskozhayeva, Akbota Bugibayeva, Almagul Kurmanova, Diana Sundetova, Merey Abdykassymova, Ulzhas Sagalbayeva

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The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022. by Maggie S J McCarter, Stella Self, Kyndall C Dye-Braumuller, Christopher Lee, Huixuan Li, Melissa S Nolan

“…An integrated nested Laplace approximation approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. …”
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A Novel Energy Control Digital Twin System with a Resource-Aware Optimal Forecasting Model Selection Scheme by Jin-Woo Kwon, Anwar Rubab, Won-Tae Kim

“…It employs a two-stage approach: first, it identifies promising models through similarity detection in past time series; second, this initial recommendation is refined by considering the available computing resources to pinpoint the optimal forecasting model. …”
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Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model by Jiali Zhang, Qinglan Li, Liguang Wu, Qifeng Qian, Xuyang Ge, Sam Tak Wu Kwong, Yun Zhang, Xinyan Lyu, Guanbo Zhou, Gaozhen Nie, Pak Wai Chan, Wai Kin Wong, Linwei Zhu

“…Compared with the best deterministic model with the detection probability (POD) of 21 % and false alarm rate (FAR) of 50 % for 24-h RI forecasts in the NA basin during 2016–2020, our ensemble model demonstrated significant improvements, achieving a POD of 0.27 and an FAR of 0.18 for the same period. …”
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Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. by Mohammed Majeed Hameed, Adil Masood, Aadil Hamid, Ahmed Elbeltagi, Siti Fatin Mohd Razali, Ali Salem

“…Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. …”
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A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators by Jiahui Long, Xiang Jia, Bingyi Li, Lin Zhu, Miao Wang

“…Secondly, a hybrid forecasting model integrating a Static Representation Module and a Dynamic Temporal Disentanglement and Attention Module for static and dynamic data fusion is proposed. …”
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Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants by Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci, Nunzia Fontana

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A Hybrid Deep Multistacking Integrated Model for Plant Disease Detection by Majdi Khalid, MD. Alamin Talukder

“…In this study, we introduce a deep multistacking integrated model for plant leaf disease detection that leverages fine-tuned transfer learning (TL) models, multistacking feature generation, and an ensemble XGBoost meta-classifier. …”
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Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems by Darío Muyulema-Masaquiza, Manuel Ayala-Chauvin

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Timeseria: An object-oriented time series processing library by Stefano Alberto Russo, Giuliano Taffoni, Luca Bortolussi

“…Timeseria comes with a comprehensive set of base data structures, data transformations for resampling and aggregation, common data manipulation operations, and extensible models for data reconstruction, forecasting and anomaly detection. …”
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