论文信息 - A comparison of extreme gradient boosting, SARIMA, exponential smoothing, and neural network models for forecasting rainfall data - 字舞流文

A comparison of extreme gradient boosting, SARIMA, exponential smoothing, and neural network models for forecasting rainfall data

I G N M Jaya | R Agata | I. Jaya | R. Agata

[1] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2] Hui Lin,et al. A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis , 2007, Comput. Geosci..

[3] Nowrouz Kohzadi,et al. A comparison of artificial neural network and time series models for forecasting commodity prices , 1996, Neurocomputing.

[4] Ashish Sharma,et al. An application of artificial neural networks for rainfall forecasting , 2001 .

[5] Faisal Zaman,et al. A rainfall forecasting method using machine learning models and its application to the Fukuoka city case , 2012, Int. J. Appl. Math. Comput. Sci..

[6] Rob J. Hyndman,et al. Forecasting with Exponential Smoothing , 2008 .

[7] Wei-Chiang Hong,et al. Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..

[8] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[9] Julián A. Pucheta,et al. A Statistically Dependent Approach for the Monthly Rainfall Forecastfrom One Point Observations , 2008, CCTA.