Download pdf
Integrating GBDT regression and factor analysis for accurate prediction of used sailboat prices
- 1 Northeastern University at Qinhuangdao
- 2 Northeastern University at Qinhuangdao
- 3 Jiangxi Normal University
- 4 Northeastern University at Qinhuangdao
* Author to whom correspondence should be addressed.
Abstract
Similar to many luxury goods, the value of sailboats fluctuates with the age of the boat and market conditions. To comprehensively understand the sailboat market, our team utilized web crawlers to collect data on second-hand sailboats and relevant regional factors (economic, geographic, tourism). We established a unified prediction model that integrates sailboat characteristics and regional influences. Using a factor analysis model, combined with a Gradient Boosting Decision Tree (GBDT) regression model, we evaluated the regional impact on second-hand sailboat prices. The combined model demonstrated high accuracy in predicting the prices of monohull and catamaran sailboats. The study further explored the applicability of the model to the Hong Kong market and validated its effectiveness with different datasets. The results indicated substantial demand and tremendous market potential in the Hong Kong market. Finally, through ARIMA time series forecasting, statistical histograms, and correlation analysis, the study revealed relationships between year and price, regional disparities, and key price-influencing factors.
Keywords
Used Sailboat Prices, Factor analysis, GBDT regression, Significance test, ARIM
[1]. Chen Y, Zheng P, Xu Y, et al. Used Sailboat Price Prediction Model Based on SSA Optimized XG-Boost Algorithm [C]//2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2023: 650-655.
[2]. Zhang J and Zhang Z. Modeling and solving used sailboat market strategy and pricing method [J]. Highlights in Business, Economics and Management, 2023, 16: 612-620.
[3]. Li L, Dai S, Cao Z, et al. Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction [J]. The Journal of Supercomputing, 2020, 76: 6887-6900.
[4]. Ding Y, Wang Z, Sun J. Research and Analysis Based on Pricing of Used Sailboats in the World and Hong Kong [J]. Highlights in Business, Economics and Management, 2023, 19: 30-37.
[5]. Yang C, Tang S, Chen J H. An Estimation of the Pricing of Second-Hand Sailboats Based on the Random Forest Algorithm [C]//Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China. 2023.
[6]. Zhou F, Zhang Q, Sornette D, et al. Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices [J]. Applied Soft Computing, 2019, 84: 105747.
[7]. Xie Y, Li X, Ngai E W T, et al. Customer churn prediction using improved balanced random forests [J]. Expert Systems with Applications, 2009, 36(3): 5445-5449.
[8]. Sun X, Liu M, Sima Z. A novel cryptocurrency price trend forecasting model based on LightGBM [J]. Finance Research Letters, 2020, 32: 101084.
[9]. Zhou F, Zhang Q, Sornette D, et al. Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices [J]. Applied Soft Computing, 2019, 84: 105747.
[10]. Hamilton J D. Time series analysis [M]. Princeton university press, 2020.
Cite this article
Yang,H.;Yao,R.;Zhu,Q.;Huang,J. (2024). Integrating GBDT regression and factor analysis for accurate prediction of used sailboat prices. Theoretical and Natural Science,31,192-206.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).