Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models by Ting Wang, David Fletcher, Matthew Parry, Craig J. Rodger, Andy W. Smith, Tanja Petersen

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Forecasting Use of Critical Rear-Earth Metals and Lithium During Energy Transformation by V. E. Shunkov, P. I. Sevostyanov

“…In spite of importance of rear-earth metals forecasting of their future use is still a complicated task. …”
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Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI by Ovanes Petrosian, Yuyi Zhang

“…The application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. …”
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Informational and Analytical Systems for Forecasting the Indicators of Financial Security of the Banking System of Ukraine by Nadiia Davydenko, Yuliya Lutsyk, Alina Buriak, Liudmyla Vovk

“…As a result, forecast models were built for each of the indicators and also, it has been found that most indicators of the banking system of Ukraine in 2021-2023 will remain at “unsatisfactory” and “critical” levels. …”
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Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model by Yanlong Gao, Feng Xing, Lipeng Kang, Mingming Zhang, Caiyan Qin

“…Finally, experimental analysis demonstrates that the proposed model achieves the highest prediction accuracy compared to commonly used time series forecasting models.…”
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Financial Interrelations of scenario Indicators of budget Forecasting with Indicators of the Federal budget of Russia by M. E. Kosov, E. K. Voronkova, A. Yu. Chalova

“…The study is based on an abstract-logical method, including a critical analysis of the predictive values of macro-indicators adopted as the basis for the parameters of the federal budget of the Russian Federation in 2023 and the planned period of 2024 and 2025 (using the level of consumer prices and the exchange rate of the ruble as an example), establishing causal relationships between the reliability of projected budget parameters at the federal level and the state of the Russian economy, identifying possible directions for the development of approaches to forecasting initial indicators for the preparation of the federal budget. …”
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A stacked generalization approach for day ahead hourly photovoltaic power forecasting by Fatema Islam Tania, Pinki Rani, Tofael Ahmed, Shameem Ahmad

“…Short-term PV power generation forecasting relies on some key meteorological feature behaviors. …”
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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

“…Short-Term Load Forecasting (STLF) was a critical task in power system operations, enabling efficient energy management and planning. …”
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A Time Series Approach to Forecasting Financial Indicators in the Wholesale and Retail Trade by Sylvia Jenčová, Petra Vašaničová, Martina Košíková, Marta Miškufová

“…Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. …”
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Short-term Power Load Forecasting Method of Data Center Considering PUE by WU Jin-song, ZHANG Shao feng, XU Xiang-min, LI Shu-tao, HUANG Yong, LIAO Xiao

“…In order to accurately predict the short-term power load of data centers, a short-term load forecasting model based on long- and short-term memory neural networks is proposed, which effectively compensates for the shortcomings of feed forward neural networks that cannot process the correlation information between sequences and traditional recurrent neural networks cannot remember long-term key information.Through analysis, it is concluded that the power usage effectiveness (PUE) value is correlated with the load. …”
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The Combination Application of FY-4 Satellite Products on Typhoon Saola Forecast on the Sea by Chun Yang, Bingying Shi, Jinzhong Min

“…To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). …”
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From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh by Md. Abu Saleh, H.M. Rasel, Briti Ray

“…Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. …”
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Concrete crack opening forecasting by back propagation neural network and differential equation by Feifei Sun, Zhonghua Xia, Weiqian Feng, Xinhua Zhu, Jinping Xie, Yu Yu, Lvlong Huang, Dong Sheng

“…Second, important results were found by field application: (1) the sole BPNN models can provide reasonable predictions; (2) better prediction can be achieved based on BPNN-DE-2TD by increasing KGE, 12% for JB-1, 37% for JB-3, and 6% for JB-7; (3) it is indicated that the addition of DE can improve the modeling on the role of air temperature under seasonal and linear trend, while BPNN part can express the nonlinear role of water level and precipitation well, confirmed by Fourier amplitude sensitivity test sensitivity and Shapley Additive exPlanations analysis. This study could provide useful insights into further forecasting of CCO under this forecast method in the world.…”
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A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer by Junrui Pan, Long Yu, Bo Zhou, Junhong Zhao

“…MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, soil temperature at 28–100 cm depth, and surface pressure as optimal features. …”
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Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics by Snehal Satish, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, Karthik Meduri, Elyson De La Cruz, Geeta Sandeep Nadella, Guna Sekhar Sajja

“…This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. …”
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Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers by Lakshmy Subramanian

“…Findings indicate the emerging trends in global health and the consequences of inaccurate demand forecasting for health supply chains. The content analysis identifies key factors that can pose a varying degree of risks for the health supply chain stakeholders. …”
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Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting by Chang-Long Jiang, Yi-Kuang Tsai, Zhen-En Shao, Shih-Hsiung Lee, Cheng-Che Hsueh, Ko-Wei Huang

“…This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. …”
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Medium-Term Minimum Demand Forecasting Based on the Parallel LSTM-MLP Model by Dosung Kim, Deukyoung Lee, Sung-Kwan Joo, Young-Min Wi

“…In particular, the proposed framework improves the forecasting accuracy by using forecasted energy consumption information. …”
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Forecasting the number of identified information security vulnerabilities based on the theory of “Gray Systems” by A. O. Efimov, S. A. Mishin, E. A. Rogozin

“…The initial data for forecasting is information obtained from the CVE (Common Vulnerabilities and Exposures) vulnerability database. …”
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FBiLSTM-Attention short-term load forecasting based on fuzzy logic by Yan ZHANG, Zepeng KANG, Xiaozhi GAO, Nan YANG, Zhaolei WANG

“…Aiming at the problem of high uncertainty in power load data due to various factors, a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting. …”
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