准确的总消费者价格指数预测与数据增强,多变量特征和情绪分析:韩国的一个案例研究
Injae Seo1, Minkyoung Kim1, Jong Wook Kim2
1Graduate School of Information, Yonsei University, Seoul, Republic of South Korea.
PloS one
|May 13, 2025
概括
准确预测韩国总消费者价格指数 (CPI) 对经济稳定至关重要. 一个新的混合深度学习模型通过分析复杂的模式和外部因素来提高CPI预测的准确性.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 数据科学数据科学数据科学
背景情况:
- 消费者价格指数 (CPI) 是影响韩国货币政策和经济稳定的关键经济指标.
- 准确的CPI预测对于政策制定者来说至关重要,但由于数据异质性,稀疏性和外部波动性而面临挑战.
研究的目的:
- 开发一个新的框架,以提高韩国总CPI预测的准确性.
- 解决现有方法在捕捉复杂的价格动态和外部影响方面的局限性.
主要方法:
- 开发了一种混合卷积神经网络-长期短期记忆 (CNN-LSTM) 模型,以捕捉CPI数据中的复杂模式.
- 多变量输入,包括CPI成分指数和辅助变量,用于更丰富的上下文信息.
- 数据增强技术 (线性插值) 将月度数据转换为每日数据,并将新闻文章的情绪指数纳入.
主要成果:
- 与现有方法相比,拟议的框架在CPI预测方面表现优越.
- 下根平均平方误差 (RMSE) 值表明估计准确度有所提高.
- 该模型有效地整合了各种数据源,包括情绪分析,以提高预测.
结论:
- 新的混合深度学习框架显著提高了韩国总CPI预测的准确性.
- 预测准确度的提高支持制定更有效和更及时的经济政策.
- 这种方法为应对通胀监测和经济管理的复杂性提供了可靠的解决方案.
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