The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market by Vasileios Laitsos, Georgios Vontzos, Paschalis Paraschoudis, Eleftherios Tsampasis, Dimitrios Bargiotas, Lefteri H. Tsoukalas

“…Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. …”
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The Relevance of Spatial and Temporal Connections and Relationships for the Formation of a Subtheory of Forensic Forecasting by I. V. Ustinova

“…The article examines the influence of spatial and temporal factors on forming a subtheory of forensic forecasting. The historical analysis of the formation of scientific knowledge about spatial and temporal connections and relationships in the light of the development of the general theory of forensic examination has been carried out. …”
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Application of big data method in forecasting potential sensitive customers of electric power by Xiaofeng CHEN, Yadi ZHAO, Lipeng ZHANG, Feng ZHU

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Forecasting and Strategic Goal-Setting Mechanisms of Social-Economic Development of the Russian Federation by I. S. Bobryshev, A. V. Vedernikova, V. V. Voronin, N. V. Erina, D. A. Tyupyshev

“…In this article, the authors carried out the analysis of principle implementation and application efficiency of strategic forecasting and strategic goal-setting mechanisms in the field of social-economic development. …”
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A survey on the application and research progress of large language models in financial forecasting by Ruonan Wu, Hong Liu

“…Large language models (LLMs) are reshaping the technical paradigms of financial forecasting through their robust representation learning and reasoning capabilities. …”
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Long-Term Science and Technology Forecasting: Methodologies, Future Visions, Scenarios of Development by Dmitry Belousov, Igor Frolov

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Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test by Chuanjin Jiang, Jing Zhang, Fugen Song

“…After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.…”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. …”
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Research on Forecasting Methods of Agricultural Products Consumption Behavior Based on Unsupervised Learning by Hui Yang, Tian Yang, Xiangxin Ma, Jia Lin

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A Novel Approach to Wavelet Neural Network-Based Wind Power Forecasting by Fedora Lia Dias, Anant J. Naik

“…In this paper, a novel Artificial Neural Network (ANN) approach using wavelet analysis for wind energy forecasting is proposed and tested with wind data from Kanyakumari, India, for different seasons. …”
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Auditing the fairness of the US COVID-19 forecast hub's case prediction models. by Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Nekabari Sigalo, Vanessa Frias Martinez

“…In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. …”
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Mid-and-Long Term Load Forecasting Based on Integrated Power Consumption Data by Xingang WANG, Binruo ZHU, Zhen GU

“…Load forecasting is critical for management and security of smart grid system. …”
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The forecasting of operating results of fish processing enterprises: problems and methodological approaches to their solving by O. А. Shikhova, M. N. Selina, V. M. Selin

“…Herewith the analysis of the results of forecasting using the received models showed that the forecast accuracy, both point and interval with a weakening of the strength of the manifestation of the correlation dependence between the signs does not decrease. …”
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Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network by Qiyue Huang, Yuqing Zheng, Yuxuan Xu

“…Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. …”
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Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models by Wang Hao

“…In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. …”
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TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-Term Series Forecasting by Jiayi Fan, Bingyao Wang, Dong Bian

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A Study on Short-Term Water-Demand Forecasting Using Statistical Techniques by Jungwon Yu, Hyansu Bae, Mi-Seon Kang, Kwang-Ju Kim, In-Su Jang

“…In the proposed method, training datasets are prepared through exploratory data analysis, several data preprocessing steps, and an input selection step; also, forecasting models are constructed by support vector regression. …”
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Long-term forecasting of international relations as strategic planning of national security policy by A. I. Podberezkin, M. V. Kharkevich

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The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model by Justina Krevnevičiūtė, Arnas Mitkevičius, Darius Naujokaitis, Ingrida Lagzdinytė-Budnikė, Mantas Marčiukaitis

“…The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. …”
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Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model by ZHANG Yajie, CUI Dongwen

“…To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,the monthly runoff with good correlation before the forecast month is selected as the influencing factor of forecasts,and the influencing factor is reduced in dimension by principal component analysis (PCA).The kernel width factor and hyperparameters of RVM are optimized by the GEO algorithm,and the GEO-RVM model is built to forecast the monthly runoff of the station during the dry season from November to April of the following year.Moreover,the forecast results are compared with those of the GEO-based support vector machine (SVM) model (GEO-SVM).The results demonstrate that the average relative errors of the GEO-RVM model for the monthly runoff forecasts from November to April of the following year are 8.59%,7.34%,5.97%,6.07%,5.99%,and 5.04%,respectively,which means the accuracy is better than that of the GEO-SVM model.The GEO algorithm can effectively optimize the kernel width factor and hyperparameters of RVM,and the GEO-RVM model has better forecast accuracy,which can be used for monthly runoff forecasting during dry seasons.…”
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