Support vector regression with genetic algorithms in forecasting tourism demand

By Kuan-Yu Chen, Cheng-Hua Wang. 2007

390 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/3a7943b2197da191e3907dbc1419982c53865998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. D. Wu
2. Shujie Shen
3. G. Crouch
4. Bo Peng
5. Haiyan Song
6. Andrea Guizzardi
7. Mario Mazzocchi
8. Gang Li

In Tourism, Leisure and Hospitality Management

1. D. Wu
2. Shujie Shen
3. G. Crouch
4. Bo Peng
5. Mario Mazzocchi
6. Andrea Guizzardi
7. Haiyan Song
8. Gang Li

Citations in the corpus

• New developments in tourism and hotel demand modeling and forecasting (2017, methodology): “SVR has been applied to tourism and hotel forecasting by several studies (e.g. Cang, 2014; Chen and Wang, 2007; Hong et al., 2011; Xu et al., 2009).”
• Tourism demand for Italy and the business cycle (2010, background): “This is a useful extension, because only few studies in the literature (Chen & Wang, 2007) assess the forecasting performance with anything other than descriptive indicators, which are very sensitive to the chosen outof-sample time span and to the specific data-set.”
• Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability (2017, result): “In general, the prediction performance of SVM-based models is superior to that of neural network-based models in forecasting data with high volatility such as net profit and operating profit [3,7,32,33,35,37].”
• Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, background): “Despite the promising SVR demonstrated in [21-24], the applications of SVR in interval-valued time series (ITS) have not been widely explored.”
• A meta-analysis of international tourism demand forecasting and implications for practice (2014, methodology): “Chen and Wang (2007) incorporate the Genetic Algorithm (GA) technique into SVR to form a GA-SVR model to forecast tourist arrivals to China, and compare the forecasting performance of GA-SVR, Back Propagation Neural Network (BPNN, the most popular ANN model) and ARIMA models.”