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.

scholarly journals Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation

Polymers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Yiyi Zhang ◽  
Xin Li ◽  
Jiake Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Dissolved Gases ◽  
Dissolved Gas ◽  
Simulated Case

A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods.

scholarly journals Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ping-Feng Pai ◽  
Ling-Chuang Hong ◽  
Kuo-Ping Lin
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Stock Markets ◽  
Google Trends ◽  
Support Vector ◽  
Internet Search ◽  
Support Vector Regression Model ◽  
Promising Alternative ◽  
Market Values ◽  
Hybrid Data

Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users’ definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.

Turbo-Shaft Engine Model Compensation Based on LSSVR

2012 ◽  
Vol 246-247 ◽  
pp. 738-743
Author(s):  
Feng Lu ◽  
Yi Qiu Lv ◽  
Wei Lin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Support Vector Regression ◽  
Correction Method ◽  
Support Vector ◽  
Simulation Studies ◽  
Model Errors ◽  
Engine Model ◽  
Modeling Errors ◽  
On Line

Considering the larger modeling errors between the turbo-shaft engine and on-board model, the model correction method based on least squares support vector regression is proposed. Firstly, the modeling principle of on-board turbo-shaft engine model is introduced, and then the structure of model combined with a compensation module is designed. The algorithm of LSSVR is used to build up the model compensation module, which is trained off-line and corrected on-line. Simulation studies on turbo-shaft engine have shown that the LS-SVM method can effectively reduce the model errors, and comparison with the interpolation correction, neural network one, the method proposed has better precision.

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

2012 ◽  
Vol 630 ◽  
pp. 366-371 ◽  
Author(s):  
Kuo Ping Lin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Support Vector Regression ◽  
Performance Prediction ◽  
Pso Algorithm ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Linear Kernel ◽  
Performance Forecasting ◽  
General Regression

The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore, the LS-SVR (Linear kernel) with PSO model can efficiently provide credible CPU performance estimated value.

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