Seasonal forecasting of Pan-Arctic sea ice with state space model by Wei Wang, Weidong Yang, Lei Wang, Guihua Wang, Ruibo Lei

“…Through comparative analysis of 25 renowned forecast models, including dynamical, statistical, and deep learning approaches, our experimental results indicate that IceMamba delivers excellent seasonal forecasting capabilities for Pan-Arctic sea ice concentration. …”
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Medium-Term Hourly Electricity Tariff Forecasting Using Ensemble Models by Matrenin P.V., Arestova A.Yu., Antonenkov D.V.

“…Hourly data on the retail market tariffs of the Novosibirsk region (Siberia) for four years were collected, several machine learning models were applied, and an analysis of the input parameters for forecasting was carried out. …”
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Time Series Forecasting Based on Temporal Networks Evolution and Dynamic Constraints by Yunlong Peng, Han Li, Xu Han

“…Time series forecasting holds significant application value in financial market analysis, biological prediction, and other domains. …”
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Forecasting the Sugarcane Yields Based on Meteorological Data Through Ensemble Learning by Sumit Kumar, Millie Pant, Atulya Nagar

“…Unexpected fluctuations in production can affect farmers’ income and the stability of the market, emphasizing the necessity of accurate forecasting to avoid adverse economic consequences. …”
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The ecological forecast limit revisited: Potential, absolute and relative system predictability by Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington, William J. Castillo, Florian Pappenberger, Carsten F. Dormann

“…Within our framework, forecast limits are defined for practically any ecological forecast and support research on ecological predictability analysis.…”
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Integrating information from historical data into mechanistic models for influenza forecasting. by Alessio Andronico, Juliette Paireau, Simon Cauchemez

“…Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). …”
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A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques by Mamunur Rashid, Tarek Elfouly, Nan Chen

“…We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. …”
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Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach by Sophia Park, Myeong Jun Kim

“…Out-of-sample multi-period forecasts were evaluated for each model using the RMSE, MAE, and Giacomini–White test to determine the most effective forecasting approach. …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. …”
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Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas by Cosmina-Mihaela Rosca, Madalina Carbureanu, Adrian Stancu

“…For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. …”
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Monitoring and Forecasting the Dynamics of the Incidence of COVID-19 in Moscow: 2020–2021 by A. V. Sokolov, L. A. Sokolova

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Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk, Marko Valčić

“…The analysis reveals distinct variable-specific uncertainty trends with wind speed forecasts exhibiting significant temporal fluctuation (RMSE increasing from 0.5 to 4.0 m/s), while significant wave height forecasts degrade in a more stable and predictable pattern (from 0.2 to 0.9 m). …”
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Microgrid energy management strategy considering source-load forecast error by Kaikai Zhang, Guibin Zou, Jinliang Zhang, Houlei Li, Yazhong Sun, Guoliang Li

“…The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management.…”
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A Large-Scale Empirical Study of Aligned Time Series Forecasting by Polina Pilyugina, Svetlana Medvedeva, Kirill Mosievich, Ilya Trofimov, Alina Kostromina, Dmitry Simakov, Evgeny Burnaev

“…Through a comprehensive empirical evaluation and comparative analysis, this study investigates the performance of 35 time series forecasting methods and scrutinizes the forecast quality and time series characteristics. …”
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On solving problems of operational forecasting of main pipeline weld joint quality by A. E. Filyakov, M. A. Sholokhov

“…The major obstacle to the successful functioning of the operational forecasting module, apart from the length of the numerical solution of equations, is an estimation error. …”
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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment by Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar

“…The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. …”
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Disulfidptosis-related LncRNAs forecast the prognosis of acute myeloid leukemia by Pei Xu, Xiaolin Sun, Lingxiao Pan, Jianfeng Zhu, Sixuan Qian

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Transformer based models with hierarchical graph representations for enhanced climate forecasting by T. Bhargava Ramu, Raviteja Kocherla, G. N. V. G. Sirisha, V. Lakshmi Chetana, P. Vidya Sagar, R. Balamurali, Nanditha Boddu

“…Abstract Accurate climate predictions are essential for agriculture, urban planning, and disaster management. Traditional forecasting methods often struggle with regional accuracy, computational demands, and scalability. …”
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Assessment of the possibility of forecasting hydrometeorological extremes using the solar activity index by Miskevich I.V., Kotova E.I.

“…In this case, the reliability of determining the error of the predictions increases. Statistical analysis of correlation fields divided on certain intervals of solar activity allows obtaining forecasts of hydrometeorological extremes capable of forming geoecological anomalies in a number of situations. …”
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Variability of ETAS parameters and their relationship with physical processes for earthquake forecasting in Africa by Thystere Matondo Bantidi, Takeshi Nishimura, Takeo Ishibe, Bogdan Enescu, Georges Mavonga Tuluka

“…Abstract The epidemic-type aftershock sequence (ETAS) model is commonly used for seismic risk assessment and earthquake forecasting. It incorporates physically interpretable parameters that control the behavior of offsprings. …”
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