THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES by Windy Ayu Pratiwi, Anwar Fajar Rizki, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

“…In the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. …”
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Forecasting Financial Literacy Levels with Respect to Consumer Shopping Behaviour by Eva Kicova, Lucia Svabova, Olga Ponisciakova, Zuzana Rosnerova

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An Informer Model for Very Short-Term Power Load Forecasting by Zhihe Yang, Jiandun Li, Haitao Wang, Chang Liu

“…., Very Short-Term Power Load Forecasting. As a time series forecasting problem, the primary challenge of VSTLF is how to identify potential factors and their very long-term affecting mechanisms in load demands. …”
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Classifying electric vehicle adopters and forecasting progress to full adoption by Trisha V. Ramadoss, Jae Hyun Lee, Adam Wilkinson Davis, Scott Hardman, Gil Tal

“…Abstract Electric light-duty vehicle sales are increasing, but adoption is not uniform. Forecasting who is adopting and when is crucial to planning infrastructure, creating incentives, and ensuring equity. …”
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Supervised filters for EEG signal in naturally occurring epilepsy forecasting. by Francisco Javier Muñoz-Almaraz, Francisco Zamora-Martínez, Paloma Botella-Rocamora, Juan Pardo

“…Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. …”
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Aerosol, Clouds and Radiation Interactions in the NCEP Unified Forecast Systems by Anning Cheng, Fanglin Yang

“…In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. …”
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Deep context-attentive transformer transfer learning for financial forecasting by Ling Feng, Ananta Sinchai

“…An ablation study highlights the impact of architectural refinements and rotary positional encoding, while prediction horizon analysis confirms stable forecasting performance. These results establish 2CAT as a robust financial forecasting framework adaptable to diverse market conditions.…”
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Feasibility of Using Statistical Forecasting Method in the Marcal Catchment Area by Máté Szabó, Katalin Bene, Gábor Kerék

“…Multi-level regression analysis, incorporating first- and second-order polynomials, was used to predict flood peaks at this outflow. …”
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Stock return forecasting based on the proxy variables of category factors by Yuan Zhao, Xue Gong, Weiguo Zhang, Weijun Xu

“…Improving the accuracy of stock return prediction and quantifying the impact of individual factors on forecasting remain challenging tasks. Motivated by these challenges, we propose a novel forecasting method that entails proxy variables of category factors and the random forest technique. …”
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Forecasting the use of chiropractic services within the Veterans Health Administration. by Victoria A Bensel, Kelsey Corcoran, Anthony J Lisi

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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. …”
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Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires by Paraskevi Oikonomou, Vassilia Karathanassi, Vassilis Andronis, Ioannis Papoutsis

“…This study explores the potential of NDVI for assessing and forecasting post-fire regeneration in burnt areas of the Peloponnese (2007) and Evros (2011). …”
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A data-to-forecast machine learning system for global weather by Xiuyu Sun, Xiaohui Zhong, Xiaoze Xu, Yuanqing Huang, Hao Li, J. David Neelin, Deliang Chen, Jie Feng, Wei Han, Libo Wu, Yuan Qi

“…It demonstrates the value of background forecasts in constraining the analysis during DA. FuXi Weather outperforms the European Centre for Medium-Range Weather Forecasts high-resolution forecasts beyond day one in observation-sparse regions such as central Africa, highlighting its potential to improve forecasts where observational infrastructure is limited.…”
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Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting by Mustapha Ez-zaiym, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, Vasile Palade

“…This novel approach leverages the memory-retentive properties of fractional calculus to improve convergence behavior and model efficiency. Our experimental analysis evaluates the performance of fractional-order optimizers on LSTM networks tasked with forecasting stock prices for major companies such as AAPL, MSFT, GOOGL, AMZN, META, NVDA, JPM, V, and UNH. …”
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Long-term Stochastic Forecasting of the Nuclear Energy Global Market by Vladimir Kharitonov, Uliana Kurelchuk, Sergey Masterov

“… This article looks at the problem of devising a long-term developmental forecast of the nuclear energy market and the possibility of studying certain sections of the market. …”
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A Vegetable-Price Forecasting Method Based on Mixture of Experts by Chenyun Zhao, Xiaodong Wang, Anping Zhao, Yunpeng Cui, Ting Wang, Juan Liu, Ying Hou, Mo Wang, Li Chen, Huan Li, Jinming Wu, Tan Sun

“…This study conducts a comprehensive analysis of the performance of traditional methods, deep learning approaches, and cutting-edge large language models in vegetable-price forecasting using multiple predictive performance metrics. …”
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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Results showed that NeuralProphet effectively captured annual and seasonal trend changes compared to the traditional singular spectrum analysis method. Beyond NeuralProphet, I also tested two recently developed state-of-the-art forecasting models—time-series dense encoder (TiDE) and neural hierarchical interpolation for time series (NHiTS)—to predict marine soundscapes. …”
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Evaluation of the 2022 West Nile virus forecasting challenge, USA by Ryan D. Harp, Karen M. Holcomb, Renata Retkute, Alisa Prusokiene, Augustinas Prusokas, Zeynep Ertem, Marco Ajelli, Allisandra G. Kummer, Maria Litvinova, Stefano Merler, Ana Pastore y Piontti, Piero Poletti, Alessandro Vespignani, Andre B. B. Wilke, Agnese Zardini, Kelly Helm Smith, Philip Armstrong, Nicholas DeFelice, Alexander Keyel, John Shepard, Rebecca Smith, Andrew Tyre, John Humphreys, Lee W. Cohnstaedt, Saman Hosseini, Caterina Scoglio, Morgan E. Gorris, Martha Barnard, S. Kane Moser, Julie A. Spencer, Maggie S. J. McCarter, Christopher Lee, Melissa S. Nolan, Christopher M. Barker, J. Erin Staples, Randall J. Nett, Michael A. Johansson

“…Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. …”
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Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis by Daniel Papla, Rafał Siedlecki

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Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis by Masoumeh Vali, Hossein Motahari Nezhad, Levente Kovacs, Amir H Gandomi

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