Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-Variance by Yuping Song, Yuetong Zhang, Po Ning, Jiayi Peng, Chunyu Kao, Liang Hao

“…An in-depth study of RDS forecasting is of great value to capture the characteristics of downside risks, enrich the financial risk measurement system, and better evaluate potential losses.…”
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A New Empirical Model for Short-Term Forecasting of the Broadband Penetration: A Short Research in Greece by Salpasaranis Konstantinos, Stylianakis Vasilios

“…In conclusion, comparing these models with the empirical model, it could be argued that the latter yields well enough statistics indicators for fitting and forecasting performance. It also stresses the need for further research and performance analysis of the model in other more mature broadband markets.…”
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Review of the Epidemiological Situation on Ixodidae Tick-Borne Borrelioses in the Russian Federation in 2010–2024 and Forecast for 2025 by S. A. Rudakova, O. E. Teslova, N. E. Mutalinova, N. V. Rudakov, N. A. Pen’evskaya, Yu. F. Kuz’menko, D. V. Trankvilevsky

“…The aim of the review was to characterize the epidemiological situation on Ixodidae tick-borne borreliosis (ITBB) in the constituent entities of the Russian Federation in 2024, to forecast the development of the ITBB epidemic process in 2025 based on the analysis of its trends over the period of 2010–2024 with the exception of 2020–2021 – the period of the COVID-19 pandemic. …”
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Time Series Forecasting Using Recurrent Neural Networks Based on Recurrent Sigmoid Piecewise Linear Neurons by Victor Sineglazov, Vladyslav Horbatiuk

“…In addition to theoretical analysis experiments on real-world time series were performed to evaluate networks with different structures and neuron types. …”
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A New Approach to Assessing the Accuracy of Forecasting of Emergencies with Environmental Consequences Based on the Theory of Fuzzy Logic by Eduard Tshovrebov, Vladimir Moshkov, Irina Oltyan and Filyuz Niyazgulov

“…In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. …”
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The tide forecasting system for China coastal seas: A case study on the effect of tides on storm surge by Yifei Zhao, Zengan Deng, Jingsheng Zhai, Ting Yu, Hu Wang, Yu Cao, Yanbin Tong

“…TFVS is capable of forecasting the tides (conducting harmonic analysis on the contrast) by invoking the T_TIDE package. …”
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Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19 by Yanhui Chen, Minglei Zhang, Kai Lisa Lo, Jackson Jinhong Mi

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Confidence interval forecasting model of small watershed flood based on compound recurrent neural networks and Bayesian. by Songsong Wang, Ouguan Xu

“…Flood forecasting exhibits rapid fluctuations, water level forecasting shows great uncertainty and inaccuracy in small watersheds, and the reliability and accuracy performance of traditional probability forecasting is often unbalanced. …”
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Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network by Yeong Nain Chi, Orson Chi

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The effects of climate change on EO/IR propagation using CMIP6 global atmospheric forecasting simulations by Parker Coye, Adam Willitsford

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Features of Forecasting Reliability of 6—10 kV Overhead Lines According to Statistics of their Failures and Reconditionings by Basmanov V.G.

“…This work is aimed at forecasting justification of the failure time of the 6—10 kV overhead elecric lines (OEL) during the normalized period in its operation based on comparison with the statistics of failures and reconditionings on the previous intervals with the use of the OEL availability function, statistical availability coefficient, normalized forecasting interval and the accepted values of the availability coefficient on the forecasting interval. …”
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Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets by Dohyun Chun, Jongho Kang, Jihun Kim

“…We construct international asset allocation portfolios based on these forecasts and evaluate their performance. Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns, as evidenced by their superior out-of-sample R-squared values. …”
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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Accurate and efficient demand forecasting of customer energy services is crucial for quality and risk management in grid customer service. …”
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Accelerating flood warnings by 10 hours: the power of river network topology in AI-enhanced flood forecasting by Hongjun Wang, Jiyuan Chen, Yinqiang Zheng, Xuan Song

“…Furthermore, the incorporation of graph information significantly enhances long-term forecasting capabilities, as evidenced by the fact that, on average, GNN predictions of water levels at 24 h after using the dense graph match the accuracy of EA-LSTM’s 14-h forecasts. …”
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Evaluating LSTM and NARX neural networks for wind speed forecasting and energy optimization in Tetouan, Northern Morocco by Wissal Masmoudi, Abdelouahed Djebli, Faouzi Moussaoui

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Time series forecasting with exogenous variables: a literature review to identify promising gaps in computational research by Rafael Diniz Toscano de Lima, Sergio Murilo Maciel Fernandes, Sidney Marlon Lopes de Lima

“…This study presents a comprehensive literature review on integrating exogenous variables in time series forecasting, with a particular focus on financial markets. …”
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Building a Sustainable GARCH Model to Forecast Rubber Price: Modified Huber Weighting Function Approach by Intan Martina Md Ghani, Hanafi A Rahim

“…The analysis incorporates two dispersion measurements (IQR/3 and Sn) and three levels of IO contamination 0%, 10%, and 20%. …”
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SCHWECHAT AIRPORT DEVELOPMENT FORECAST AS A RISK MANAGEMENT TOOL TO ADDRESS THE IMPACT OF THE COVID-19 PANDEMIC by Robert ROZENBERG, Marek TOMAS, Alica TOBISOVA, Peter HANAK

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Mathematical Evaluation of Classical and Quantum Predictive Models Applied to PM2.5 Forecasting in Urban Environments by Jesús Cáceres-Tello, José Javier Galán-Hernández

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Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process by Mohammed Alruqimi, Luca Di Persio

“…This paper introduces a Crude Oil-Driven Conditional GAN (CO-CGAN), a hybrid model for enhancing crude oil price forecasting by combining advanced AI frameworks (GANs), oil market sentiment analysis, and stochastic jump-diffusion models. …”
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