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An integrated method based on relevance vector machine for short-term load forecasting
Ding, Jia; Wang, Maolin; Ping, Zuowei; Fu, Dongfei; … - In: European journal of operational research : EJOR 287 (2020) 2, pp. 497-510
Persistent link: https://www.econbiz.de/10012293858
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Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model
Srivastava, Prashant; Han, Dawei; Rico-Ramirez, Miguel; … - In: Water Resources Management 27 (2013) 15, pp. 5069-5087
latest satellite soil moisture dedicated mission SMOS and WRF-NOAH Land Surface Model (WRF-NOAH LSM) provide a flow of coarse … (ANN) are evaluated for Soil Moisture Deficit (SMD) estimation using the SMOS and WRF-NOAH LSM derived soil moisture. The … first method (and most simplest) utilizes a series of simple combinations between SMOS and WRF-NOAH LSM soil moisture …
Persistent link: https://www.econbiz.de/10010794306
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Comparison of automated leaf recognition techniques
Hassan, Mahmudul; Maji, Arnab Kumar - In: International journal of intelligent enterprise 8 (2021) 2/3, pp. 205-214
Persistent link: https://www.econbiz.de/10012512542
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Machine learning in energy economics and finance: a review
Ghoddusi, Hamed; Creamer Guillén, Germán; Rafizadeh, Nima - In: Energy economics 81 (2019), pp. 709-727
Persistent link: https://www.econbiz.de/10012172954
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Is artificial intelligence really more accurate in predicting bankruptcy?
Letkovský, Stanislav; Jeňcová, Sylvia; … - In: International Journal of Financial Studies : open … 12 (2024) 1, pp. 1-19
Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success...
Persistent link: https://www.econbiz.de/10014502270
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Ground water level prediction using machine learning
J Rajaram; D Navya; E Krishna; G Harika - In: Journal of Contemporary Issues in Business and Government 26 (2020) 3, pp. 106-112
Persistent link: https://www.econbiz.de/10014250834
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Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - 2021
Persistent link: https://www.econbiz.de/10012816704
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Bankruptcy prediction using machine learning
Wang, Nanxi - In: Journal of mathematical finance 7 (2017) 4, pp. 908-918
Persistent link: https://www.econbiz.de/10011859939
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Estimation procedures of using five alternative machine learning methods for predicting credit card default
Teng, Huei-Wen; Lee, Michael - In: Review of Pacific Basin financial markets and policies 22 (2019) 3, pp. 1950021-1-1950021-27
Persistent link: https://www.econbiz.de/10012156160
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The predictability of the exchange rate when combining machine learning and fundamental models
Zhang, Yuchen; Hamori, Shigeyuki - In: Journal of risk and financial management : JRFM 13 (2020) 3/48, pp. 1-16
In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical...
Persistent link: https://www.econbiz.de/10012174126