ArticleComputational intelligence and neuroscience2018
Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting.
Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine.Entropy (Basel, Switzerland) · 2022Article
- Using Harris hawk optimization towards support vector regression to ozone prediction.Stochastic environmental research and risk assessment : research journal · 2022Article
- Article
- Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.PloS one · 2019Article
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Authors and funding
4 authors.
Funding
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Abstract
The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS-PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS-PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS-PSOSVR is an effective method for forecasting tourism demand.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.