Three Hour Ahead PV Power Forecasting with Bidirectional Recurrent Networks: Insights into Monthly Variability by Ferial El Robrini, Badia Amrouche

“…This research significantly contributes to the advancement of predictive modeling in time-series analysis, providing valuable insights into model performance and seasonal variations, and equipping practitioners and researchers with enhanced methodologies for improving forecast accuracy.…”
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Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment by Vijendra Kumar, Kul Vaibhav Sharma, Nikunj K. Mangukiya, Deepak Kumar Tiwari, Preeti Vijay Ramkar, Upaka Rathnayake

“…Finally, recommendations and future directions of ML models in flood analysis are presented.…”
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FUTURE TRENDS IN MILK FAT CONTENT: A FIVE-YEAR FORECAST FOR ROMANIA AND THE EUROPEAN UNION by Raluca-Aniela GHEORGHE-IRIMIA, Dana TĂPĂLOAGĂ, Cosmin ȘONEA, Paul-Rodian TĂPĂLOAGĂ, Silviu-Ionuț BEIA

“…The objective of this study was to employ time series analysis and regression modelling techniques in order to examine the evolution of milk fat content and evaluate the precision of predicted models. …”
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Forecasting model to predict the first occurrence of Scaphoideus titanus nymphal stages in selected locations in Austria by Kerstin Kolkmann, Gudrun Strauss, Josef Eitzinger, Sylvia Blümel

“…The recent phenology changes of S. titanus make monitoring scheduling more difficult and propose the use of an accurate forecasting model. The present study aimed to test existing forecasting models for their accuracy and applicability to predict the first seasonal occurrence of the first nymphal stage (N1) of S. titanus and to develop new prediction models for N1 and N3 in Austria for the first time. …”
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Forecasting technique of transport need for population of cities with high tourist potential during peak seasons by N. N. Yakunin, N. V. Yakunina, A. F. Fattakhova, A. A. Postnikova, M. R. Yanuchkov

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Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. by Pei-Hua Cao, Xin Wang, Shi-Song Fang, Xiao-Wen Cheng, King-Pan Chan, Xi-Ling Wang, Xing Lu, Chun-Li Wu, Xiu-Juan Tang, Ren-Li Zhang, Han-Wu Ma, Jin-Quan Cheng, Chit-Ming Wong, Lin Yang

“…In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China.…”
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A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction by Bo Li, Yuanqiang Lian

“…An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. …”
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ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS by Mariono Mariono, Syaharuddin Syaharuddin, Sameer Ashraf, Sunday Emmanuel Fadugba

“…Additionally, advanced analytical methods, like Bayesian Dynamic Linear Models and LSTM neural networks, enhance predictive accuracy when applied to sentiment analysis in this context. These findings underscore the value of social media sentiment in refining forecasting models, while also highlighting gaps in understanding regional sentiment variations and their effects on different commodity types. …”
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Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning by Nabil I. Ajali-Hernández, Carlos M. Travieso-González

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Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. by Cathy W S Chen, Leon L Hsieh, Betty X Y Chu

“…Using an expanding window approach, the analysis applies Bayesian structural time series (BSTS) models, exponential smoothing, and random forest to forecast one-week-ahead cases over the 27 weeks in 2024. …”
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Graph-Based Stock Volatility Forecasting with Effective Transfer Entropy and Hurst-Based Regime Adaptation by Sangheon Lee, Poongjin Cho

“…This study proposes a novel hybrid model for stock volatility forecasting by integrating directional and temporal dependencies among financial time series and market regime changes into a unified modeling framework. …”
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Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026 by Nur Anggraini Fadhilah, Muhammad Abshor Dzulhij Rizki, Muhammad Ryan Azahran, Siti Arbaynah, Rakesha Putra Antique Yusuf, Yenni Angraini, Muhammad Rizky Nurhambali

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Development and Implementation of a Machine Learning‐Based Flood Forecasting System in Kasese District, Uganda by Edward Miiro, Ismael Kato, Zuhra Nantege, Samuel Ssendi, Khasim Bassajjalaba

“…ABSTRACT This study aimed to develop a proof‐of‐concept prototype of a machine learning system to forecast and mitigate the effect of floods in Kasese District. …”
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A Multi-Scale Time–Frequency Complementary Load Forecasting Method for Integrated Energy Systems by Enci Jiang, Ziyi Wang, Shanshan Jiang

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Forecasting of heat and mass transfer in Casson nanofluid flow with entropy optimization: Machine learning approach by P. Kavya, K. Loganathan, K. Chellapriya, S. Eswaramoorthi, S. Divya, Krishna Prakash Arunachalam, P. Senthilkumar

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Forecasting pandemic-induced changes in real estate market values through machine learning approaches by Adile Gülsüm Ulucan, Aslı Bozdağ, Murat Karakoyun, Tansu Alkan

“… In this study, a new temporal segmentation method is used to forecasting the real estate market based on the structural and spatial attributes of 676 houses in Niğde, Türkiye, from the years 2019 to 2022. …”
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Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY by Madilyn Louisa, Gumgum Darmawan, Bertho Tantular

“…It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment.…”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, forecasting, power flow analysis, and optimization of feeders for operational decision-making by individually modeling each of these numerous renewables in the absence of complete information are operationally challenging and technically impractical. …”
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Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method by Xingtao Song, Wei Han, Haofei Sun, Hao Wang, Xiaofeng Xu

“…The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts.…”
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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This spatial dependency analysis enables rapid deployment in different coastal settings. …”
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