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	71	Estimation of drought-related yield loss using the dynamic statistical model of crop productivity forecasting Enthalten in Russian meteorology and hydrology Bd. 41, 19.5.2016, Nr. 4, date:4.2016: 299-306
	72	Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models Enthalten in SN Computer Science Bd. 3, 15.2.2022, Nr. 2, date:3.2022: 1-19
	73	Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models Enthalten in Wireless personal communications Bd. 130, 10.3.2023, Nr. 2, date:5.2023: 1479-1502
	74	Forecasting methodologies for Ganoderma spore concentration using combined statistical approaches and model evaluations Enthalten in International journal of biometeorology Bd. 60, 13.8.2015, Nr. 4, date:4.2016: 489-498
	75	Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions Enthalten in Environmental monitoring and assessment Bd. 195, 23.3.2023, Nr. 4, date:4.2023: 1-14
	76	Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data Enthalten in International journal of computational intelligence systems Bd. 2, 1.12.2009, Nr. 4, date:12.2009: 353-364
	77	Hourly solar irradiance forecasting based on statistical methods and a stochastic modeling approach for residual error compensation Enthalten in Stochastic environmental research and risk assessment Bd. 37, 30.8.2023, Nr. 12, date:12.2023: 4857-4892
	78	How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods Enthalten in Internationaler Währungsfonds: IMF economic review Bd. 60, 13.3.2012, Nr. 1, date:4.2012: 75-113
	79	Hybrid Approach for Forecasting Stock Exchange Index Combining Statistical Methods and Artificial Neural Network Enthalten in Optical memory and neural networks Bd. 30, 5.10.2021, Nr. 3, date:7.2021: 194-205
	80	Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data Enthalten in Zhongnan-Daxue: Journal of Central South University Bd. 26, 25.9.2019, Nr. 8, date:8.2019: 2136-2148

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