Forecasting Municipal Solid Waste Removal Volume Based on Socioeconomic Indicators for Carbon Reduction Strategy in Beijing’s Waste Management  by Yaxin Cui, Min Yee Chin, Hong Sheng Loh, Chew Tin Lee, Pei Ying Ong, Yee Van Fan, Kok Sin Woon

“…The analysis integrates socioeconomic indicators, including population and GDP, to elucidate the complex relationship between MSW generation and economic development. …”
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A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model. by Xiangtu Li

“…To accurately predict the sales of new energy vehicles (NEVs) in Chinese cities and explore the applicability of optimization algorithms for GRU models in forecasting urban NEV sales., this paper conducts a spatiotemporal analysis of urban NEV sales data. …”
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Fast and Interpretable Probabilistic Solar Power Forecasting via a Multi-Observation Non-Homogeneous Hidden Markov Model by Jiaxin Zhang, Siyuan Shang

“…The increasing complexity and uncertainty associated with high renewable energy penetration require forecasting methods that provide more comprehensive information for risk analysis and energy management. …”
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Pink salmon fishery in the Far-Eastern fishing basin in 2022: preliminary studies, forecast, interpretation of the fishing season results by E. A. Shevlyakov, A. A. Somov, V. A. Shevlyakov, A. N. Kanzeparova, N. A. Dederer, I. V. Melnikov

“…The main stages of forecasting of the pink salmon fishery in the Far Eastern basin are described. …”
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A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India by R. L. Manogna, Vijay Dharmaji, S. Sarang

“…Abstract This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. …”
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Fusion of Sentiment and Market Signals for Bitcoin Forecasting: A SentiStack Network Based on a Stacking LSTM Architecture by Zhizhou Zhang, Changle Jiang, Meiqi Lu

“…This paper proposes a comprehensive deep-learning framework, SentiStack, for Bitcoin price forecasting and trading strategy evaluation by integrating multimodal data sources, including market indicators, macroeconomic variables, and sentiment information extracted from financial news and social media. …”
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Pink salmon fishery in the Far-Eastern fishing basin in 2023: preliminary studies, forecast, interpretation of the fishing season results by E. A. Shevlyakov, A. A. Somov, V. A. Shevlyakov, A. N. Kanzeparova, N. A. Dederer, I. V. Melnikov

“…For the forecast for the Okhotsk Sea, materials on differentiation of mixed pink salmon assemblages were analyzed by specialists of the Kamchatka branch of VNIRO, using both traditional methods (as morphological express- method and genetic method based on mt-DNA analysis) and a new method based on SNP-locus variability.…”
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SE-TFF: Adaptive Tourism-Flow Forecasting Under Sparse and Heterogeneous Data via Multi-Scale SE-Net by Jinyuan Zhang, Tao Cui, Peng He

“…Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. …”
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Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index by Yenni Angraini, Adelia Putri Pangestika, I Made Sumertajaya

“…In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.…”
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Optimized LSTM-based electric power consumption forecasting for dynamic electricity pricing in demand response scheme of smart grid by Balakumar Palaniyappan, Senthil Kumar Ramu

“…The system allows customers to efficiently schedule Price-Dependent Loads (PDL) and Electric Vehicle (EV) charging sessions by including the forecasted EPC into DDEP. The analysis is done on metrics for performance, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and root-mean-squared correlation (R2). …”
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Assessing SPI and SPEI for drought forecasting through the power law process: A case study in South Sulawesi, Indonesia by Nurtiti Sunusi, Nur Hikmah Auliana

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Stacking Ensemble Learning and SHAP-Based Insights for Urban Air Quality Forecasting: Evidence from Shenyang and Global Implications by Zhaoxin Xu, Huajian Zhang, Andong Zhai, Chunyu Kong, Jinping Zhang

“…Accurate air quality forecasting is essential for effective mitigation strategies, particularly in rapidly urbanizing regions. …”
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LncRNA NEXN-AS1 Serves as a Diagnostic Biomarker for Carotid Artery Stenosis, Forecasting Cerebral Ischemic Events by Weilong Liu MM, Dandan Jia MM, Lijun Yang MM, Zhenzeng Fan MD

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Forecasting cancer incidence and prevalence using age–period–cohort and survivorship models: a practical, flexible, and interpretable framework by Ana F. Best, Adalberto M. Filho, Philip S. Rosenberg

“…In this paper, we formalize methods for forecasting incidence and introduce novel forecasting methods for prevalence that are highly flexible and interpretable. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Cooling load forecasting is a crucial aspect of optimizing energy efficiency and efficient operation in central air conditioning systems for manufacturing plants. …”
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Agent-based model for forecasting the impact of the population life quality on migration movement in the context of the Russian Federation federal districts. by M. M. Nizamutdinov, Z. A. Davletova

“…Since the main incentive for active citizens to change their place of residence is investing in the development of the region and providing the necessary conditions for a comfortable life, the purpose of the study is to develop an agent-based model for forecasting the impact of the population life quality on migration flows between the federal districts of the Russian Federation. …”
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Re-calibrating measurements of low-cost air quality monitors using PCR-GPR air quality forecasting models. by Bing Liu, Shuting Yang, Junqi Wang

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A novel multivariate decomposition-based hybrid model for interpretable multi-step-ahead daily reference evapotranspiration forecasting by Ali Matoog Obaid Lebawi, Mahnoosh Moghaddasi, Mehdi Mohammadi Ghaleni, Mansour Moradi

“…Notably, the FS-OMVMD-ETE model achieved the highest accuracy for ET0 (t + 7) at Yazd and Ramsar stations. The analysis indicated that in a hyper-arid climate, the U2 feature has the greatest impact on forecasting, while in a humid climate, Tmean is the most influential factor.…”
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An electrical load forecasting model based on a novel closed loop neural networks and interaction gain feature selection by Gholamreza Memarzadeh, Faezeh Amirteimoury, Hossein Noori, Farshid Keynia

“…This multi-step process begins with the wavelet transform, which decomposes the load data into distinct frequency components, allowing for a detailed analysis of underlying patterns. Then, MI-IG was employed for feature selection, ensuring that only the most informative and relevant variables are included in the forecasting model. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…In addition, through the in-depth analysis and processing of residuals, it is demonstrated that starvation of our method further improves the credibility of the prediction results, and effectively predicts the price movements of the four major gold markets. …”
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