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Electricity price forecasting : a review of the state-of-the-art with a look into the future
Weron, Rafał - In: International journal of forecasting 30 (2014) 4, pp. 1030-1081
Persistent link: https://www.econbiz.de/10010517770
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How do less advanced forecasting methods perform on weekly RevPAR in different forecasting horizons following the recession?
Zheng, Tianshu; Bloom, Barry A. N.; Wang, Xiaofang; … - In: Tourism analysis : an interdisciplinary tourism & … 17 (2012) 4, pp. 459-472
Persistent link: https://www.econbiz.de/10009688857
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Forecasting using nonlinear long memory models with artificial neural network expansion
Chaleampong Kongcharoen - In: Uncertainty analysis in econometrics with applications …, (pp. 241-254). 2013
Persistent link: https://www.econbiz.de/10009711144
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Forecasting US tourist arrivals using optimal singular spectrum analysis
Hassani, Hossein; Webster, Allan D.; Silva, Emmanuel Sirimal - In: Tourism management : research, policies, practice 46 (2015), pp. 322-335
Persistent link: https://www.econbiz.de/10010486219
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Exchange rate forecasting through distributed Time-Lagged Feedforward Networks
Pavlidis, N. G.; Tasoulis, D. K.; Androulakis, G. S.; … - In: Supply chain and finance, (pp. 283-298). 2004
Persistent link: https://www.econbiz.de/10002540136
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Prognosemodelle und Handelsansätze für Implizite Volatilitäten
Sachtler, Michael - 2004
Persistent link: https://www.econbiz.de/10002392690
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Fusion von Expertenwissen und Daten mit Neuro-Fuzzy-Methoden zur Prognose von Finanzzeitreihen
Siekmann, Stefan - 1999
Persistent link: https://www.econbiz.de/10001423741
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Kombination künstlicher neuronaler Netze : zur Prognose von Wechselkursen
Richter, Frank - 2003 - 1. Aufl.
Persistent link: https://www.econbiz.de/10001788032
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Neuronale Netze zur Prognose von Warenterminpreisen
Stolzke, Ulf A. - 2000
Persistent link: https://www.econbiz.de/10001421515
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Structural analysis and forecast of gold price returns
Chai, Jian; Zhao, Chenyu; Hu, Yi; Zhang, Zhe George - In: Journal of management science and engineering 6 (2021) 2, pp. 135-145
Gold has multiple attributes and its price is affected by various factors in the market. This paper studies the dynamic relationship between the gold price returns and its affecting factors. Then we use the STL-ETS, neural network and Bayesian structural time series model to predict the gold...
Persistent link: https://www.econbiz.de/10013206193