Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China

2014 ◽  
Vol 64 (1) ◽  
pp. 95-104 ◽  
Author(s):  
Wenxi Lu ◽  
Haibo Chu ◽  
Zheng Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Monthly Rainfall ◽  
Jilin Province ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Rainfall Forecasting ◽  
Western Jilin Province ◽  
Generalized Regression ◽  
Western Jilin

Application of bootstrap-based neural networks for monthly rainfall forecasting in Western Jilin Province, China

2014 ◽  
Vol 9 (2) ◽  
pp. 186-196
Author(s):  
Haibo Chu ◽  
Wenxi Lu ◽  
Xiaoqing Sun
Keyword(s):  
Time Series ◽  
Absolute Error ◽  
Monthly Rainfall ◽  
Jilin Province ◽  
Generalized Regression Neural Network ◽  
Rainfall Time Series ◽  
Rainfall Forecasting ◽  
Uncertainty Estimates ◽  
Western Jilin Province ◽  
Western Jilin

Rainfall forecasting is an important pre-requisite for effectively managing and planning water resources. This study developed a generalized regression neural network (GRNN) combined with a bootstrap approach for rainfall forecasting, and the forecasting results were compared with the autoregressive model and single GRNN model. The test was performed in western Jilin Province, China with a 53-year (1957–2010) monthly rainfall time series. To obtain the good performance of GRNN model, the number of input neurons was decided by the analysis of Bayesian information criterion, and the appropriate spread was selected considering the performance of the training and testing phases. mean absolute error, root mean square error, coefficient of efficiency and R2 are employed to evaluate the performances of the forecasting models. The results showed that the bootstrap-based GRNN model performed better than single GRNN and AR models in forecasting monthly rainfall and the proposed method can improve the prediction accuracy of monthly rainfall time series, while generating uncertainty estimates of the rainfall forecasting.

scholarly journals Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 625
Author(s):  
Yi-Ting Huang ◽  
Ping-Feng Pai
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Least Squares ◽  
Support Vector Regression ◽  
Back Propagation ◽  
Analysis Data ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Data Types ◽  
Hybrid Data

Due to the rapid prominence and popularity of social media, social broadcasting networks with voluntary information sharing have become one of the most powerful ways to spread word-of-mouth opinions, and thus, have influence on consumers’ preferences toward products. Therefore, sentiment analysis data from social media have become more important in forecasting product sales. For the movie industry, the opinions expressed on social media have increasing impacts on movie sales. In addition, some databases, such as the Box Office Mojo and Internet Movie Database (IMDb), contain structured data for predicting movie sales. Thus, three categories of data—data of movie databases, data of tweets, and hybrid data including movies databases and tweets—are employed symmetrically in this study. The aim of this study is to employ the least squares support vector regression (LSSVR) to forecast movie sales worldwide according to these three forms of data. In addition, three other forecasting techniques—namely, the back propagation neural network (BPNN), the generalized regression neural network (GRNN), and the multivariate linear regression (MLR) model—were used to forecast movie sales with the three types of data. The empirical results show that the LSSVR model with hybrid data can obtain more accurate results than the other forecasting models with all data types. Thus, forecasting movie sales using the LSSSVR model with data containing movie databases and tweets is a feasible and prospective method to forecast movie sales.

A hybrid support vector regression–firefly model for monthly rainfall forecasting

2018 ◽  
Vol 16 (1) ◽  
pp. 335-346 ◽  
Author(s):  
A. Danandeh Mehr ◽  
V. Nourani ◽  
V. Karimi Khosrowshahi ◽  
M. A. Ghorbani
Keyword(s):  
Support Vector Regression ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Rainfall Forecasting
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