用隐马尔可夫模型增强LSTM，提升通胀预测准确性
HMM-LSTM Fusion Model for Economic Forecasting
- 将HMM提取的隐藏状态作为特征输入LSTM
- 模型在捕捉复杂时间模式上准确率显著提高
- 适合关注经济预测可解释性的研究者使用
本文探讨了隐马尔可夫模型（HMM）与长短期记忆（LSTM）神经网络在经济预测中的应用，重点针对消费者价格指数（CPI）通胀率进行预测。研究提出一种新方法，将HMM识别出的隐藏状态及其均值作为额外特征融入LSTM建模，旨在提升模型的可解释性与预测性能。研究首先进行数据收集与预处理，随后利用HMM识别代表不同经济状态的隐藏状态，再基于原始数据与增强数据集训练LSTM模型，开展对比分析与评估。结果表明，引入HMM衍生特征能有效提升LSTM模型的预测精度，尤其在捕捉复杂时间模式及缓解经济波动影响方面表现突出。此外，论文采用集成梯度法实现模型可解释性分析，揭示了预测结果中反映的经济动态机制。
原文摘要 · Abstract (English)
This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting, focusing on predicting CPI inflation rates. The study explores a new approach that integrates HMM-derived hidden states and means as additional features for LSTM modeling, aiming to enhance the interpretability and predictive performance of the models. The research begins with data collection and preprocessing, followed by the implementation of the HMM to identify hidden states representing distinct economic conditions. Subsequently, LSTM models are trained using the original and augmented data sets, allowing for comparative analysis and evaluation. The results demonstrate that incorporating HMM-derived data improves the predictive accuracy of LSTM models, particularly in capturing complex temporal patterns and mitigating the impact of volatile economic conditions. Additionally, the paper discusses the implementation of Integrated Gradients for model interpretability and provides insights into the economic dynamics reflected in the forecasting outcomes.
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