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拍卖项目价格预测中的深度强化学习模型:跨间隔报价策略的优化研究
Da Ke1, Xianhua Fan2
1School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, China.
PeerJ. Computer science
|August 15, 2024
概括
本研究介绍了一种使用强化学习和门式循环单位 (RL-GRU) 进行拍卖价格预测的AI框架. 这种新的方法在预测价格间隔方面达到90%以上的准确性,提高了拍卖市场的效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 经济学 经济学 经济学
背景情况:
- 拍卖行业越来越数字化,人工智能集成提高了效率和参与者的参与度.
- 准确的价格预测对于有效的拍卖市场动态和商品估值至关重要.
研究的目的:
- 通过使用先进的人工智能方法来解决拍卖中价格预测的挑战.
- 开发和评估拍卖价格区间分析的新框架.
主要方法:
- 使用门式循环单位 (GRU) 提取拍卖商品的数量特征.
- 整合强化学习 (RL) 技术用于动态环境交互.
- 一个用于区间划分和认可拍卖商品价格的分类模块.
主要成果:
- RL-GRU框架显示出高精度,在价格间隔预测中超过90%的准确性.
- 在公开可用和内部策划的数据集中观察到卓越的性能.
- 该模型有效地将拍卖价格细分为五个和八个不同的间隔.
结论:
- 开发的RL-GRU框架为拍卖价格间隔预测提供了一个强大的解决方案.
- 这种人工智能驱动的方法为优化拍卖市场运营提供了宝贵的技术见解.
- 这些发现有助于在拍卖行业推进预测分析.
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