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Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting.

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Forecasting tourism demand is vital for economic planning. A new method, feature selection-particle swarm optimization-support vector regression (FS-PSOSVR), improves accuracy in predicting tourist arrivals.

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Area of Science:

  • Economics
  • Data Science
  • Tourism Management

Background:

  • Tourism is a significant global economic driver.
  • Accurate tourism demand forecasting is essential for policy and planning.
  • Existing forecasting methods may lack optimal accuracy.

Purpose of the Study:

  • To develop an advanced forecasting tool for tourism demand.
  • To enhance prediction accuracy using a novel hybrid approach.
  • To provide reliable data for industry and government stakeholders.

Main Methods:

  • A hybrid model combining feature selection (FS) and support vector regression (SVR).
  • Particle swarm optimization (PSO) is utilized to optimize SVR parameters.
  • The integrated FS-PSOSVR method aims to improve input variable selection and model performance.

Main Results:

  • The FS-PSOSVR method was tested on monthly tourist arrival data for Taiwan (2006-2016).
  • The proposed method demonstrated significantly smaller prediction errors compared to other approaches.
  • This indicates superior performance in capturing tourism demand fluctuations.

Conclusions:

  • FS-PSOSVR is an effective and accurate method for forecasting tourism demand.
  • The study provides a valuable tool for optimizing tourism industry strategies.
  • Accurate forecasting supports better resource allocation and policy development.