Research for SARIMA and PatchTSMixer Models on the IEA Monthly Statistics Dataset by Hu Yuwei

“…The research employs a rigorous experimental design, leveraging models such as ARIMA and PatchTSMixer, with an emphasis on model tuning and performance metrics like MAE, MAPE, and RMSE. The findings reveal that deep learning models, particularly PatchTSMixer, outperform traditional machine learning methods in terms of prediction accuracy, demonstrating their superior capability in capturing complex temporal dependencies in electricity consumption data. …”
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Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches by Leu Ying Chen

“…With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bonds (T10Y2Y). …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…Results demonstrate that GA-Attention-Fuzzy-Stock-Net consistently outperforms traditional machine learning approaches and baseline models across different evaluation metrics (MSE, MAE, MAPE, R2). …”
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ECP-IEM: Enhancing seasonal crop productivity with deep integrated models. by Ghulam Mustafa, Muhammad Ali Moazzam, Asif Nawaz, Tariq Ali, Deema Mohammed Alsekait, Ahmed Saleh Alattas, Diaa Salama AbdElminaam

“…Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. …”
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Federated learning based reference evapotranspiration estimation for distributed crop fields. by Muhammad Tausif, Muhammad Waseem Iqbal, Rab Nawaz Bashir, Bayan AlGhofaily, Alex Elyassih, Amjad Rehman Khan

“…Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single specific area. …”
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Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete by Amir Reza Taghavi Khangah, Erfan Khajavi, Hasti Azizi, Amir Reza Alizade Novin

“…The results highlight hybrid machine learning models as the potential to solve complex challenges in civil engineering and provide new approaches toward sustainable and efficient infrastructure development.…”
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