Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework by Liyu Chen, Xiangwei Fan

“…Abstract Financial risk forecasting is critical for the early detection of corporate distress, yet traditional methods and recent deep learning models exhibit notable limitations. …”
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Forecasting Technology Trends through the Gap Between Science and Technology: The Case of Software as an E-Commerce Service by Tugrul Daim, Esraa Bukhari, Dana Bakry, James VanHuis, Haydar Yalcin, Xiaoli Wang

“…For that, it is important to turn to methods that are used for technology forecasting. …”
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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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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…The proposed hybrid forecasting method is validated using a dataset of electric power demand time series collected from a real-world large passenger ship. …”
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A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation by Jinliang Yao, Feifan Xu, Zheng Qian, Zhipeng Cai

“…Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).…”
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Analyses of burned area of forest by adaptive neuro-fuzzy approach by Jasmina Dedić, Srđan Jović, Jelena Đokić

“…The ANFIS process was implemented to detect the dominant factors which affect the forecasting of the burned area of forest.…”
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Effectiveness and trend forecasting of tuberculosis diagnosis after the introduction of GeneXpert in a city in south-eastern Brazil. by Thaís Zamboni Berra, Dulce Gomes, Antônio Carlos Vieira Ramos, Yan Mathias Alves, Alexandre Tadashi Inomata Bruce, Luiz Henrique Arroyo, Felipe Lima Dos Santos, Ludmilla Leideanne Limirio Souza, Juliane de Almeida Crispim, Ricardo Alexandre Arcêncio

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Maize tasseling date forecast from canopy height time series estimated by UAV LiDAR data by Yadong Liu, Chenwei Nie, Liang Li, Lei Shi, Shuaibing Liu, Fei Nan, Minghan Cheng, Xun Yu, Yi Bai, Xiao Jia, Liming Li, Yali Bai, Dameng Yin, Xiuliang Jin

“…This study proposed an approach to timely identify and forecast the maize TD. We obtained RGB and light detection and ranging (LiDAR) data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments. …”
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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

“…ConclusionsOur study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. 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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Comparison of early forecasts of the incidence of thyroid cancer in residents of the Russian Federation after the Chernobyl accident with observational data by I. A. Zvonova, M. I. Balonov

“…A review of methods for assessing doses in the thyroid gland, predictions of the long-term consequences of its irradiation and the actual incidence of thyroid cancer in residents of four regions of the Russian Federation with the most significant radioactive fallout after the Chernobyl accident are presented. …”
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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

“…Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90% accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks. …”
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Forecast of the morphology and occurrence conditions of complex chromite ore bodies (on example of Labogeyskoe-2 site, Polar Urals) by T. P. Morozova, E. V. Karelina, V. E. Markov

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MICROALBUMINURIA AS A FORECAST FACTOR FOR CARDIO-VASCULAR COMPLICATIONS AND A MARKER OF THERAPY EFFICACY IN PATIENTS WITH ARTERIAL HYPERTENSION by L. A. Haisheva, L. I. Katelnitskaya

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The Fundamentals of a Two-Stage Approach to Systematic Earthquake Prediction by Gitis Valeriy, Derendyaev Alexander

“…The proposed methodology introduces the following innovations: 1 – A prediction is considered successful if all epicenters of the target earthquakes during the forecast interval fall within the alarm zone. 2 – The methodology optimizes both the probability of successfully detecting earthquake epicenters across a series of forecasts and the success rate of predictions in each individual iteration. 3 – The methodology enables the estimation of the probability of success for the next forecast interval. …”
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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

“…Utilizing real-world LPG consumption data from 887 sensors, the proposed system achieves forecasting accuracy comparable to previous methods while reducing latency by up to 19 times in low-resource settings.…”
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Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains by Yongbin Yang, Mengdie Wang, Jiyuan Wang, Pan Li, Mengjie Zhou

“…While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. …”
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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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Efficiency of Multi-Terminators Method to Reveal Seismic Precursors in Sub-Ionospheric VLF Transmitter Signals: Case Study of Turkey–Syria Earthquakes Mw7.8 of 6 February 2023 by Mohammed Y. Boudjada, Patrick H. M. Galopeau, Sami Sawas, Giovanni Nico, Hans U. Eichelberger, Pier F. Biagi, Michael Contadakis, Werner Magnes, Helmut Lammer, Wolfgang Voller

“…This work presents an analysis of the sub-ionospheric VLF transmitter signal disturbances which were detected more than one week before the Turkey–Syria EQ occurrence. …”
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Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence by Stefania Gentili, Piero Brondi, Giuliana Rossi, Monica Sugan, Giuseppe Petrillo, Jiancang Zhuang, Stefano Campanella

“…We explored how the significant discrepancies in these methods’ results affect the result of NExt STrOng Related Earthquake (NESTORE) algorithm—a method to forecast strong aftershocks during an ongoing cluster—previously successfully applied to the whole Italian territory. …”
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Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria by Boumediene Ladjal, Mohamed Nadour, Mohcene Bechouat, Nadji Hadroug, Moussa Sedraoui, Abdelaziz Rabehi, Mawloud Guermoui, Takele Ferede Agajie

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