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.

翻译：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，重点关注消费者价格指数（CPI）通胀率的预测。研究探索了一种新方法，将HMM推导出的隐状态和均值作为LSTM建模的附加特征，旨在提升模型的可解释性和预测性能。研究从数据收集与预处理开始，随后实施HMM以识别代表不同经济状况的隐状态。接着，使用原始数据集和增强数据集训练LSTM模型，以便进行对比分析和评估。结果表明，融入HMM推导的数据提高了LSTM模型的预测准确性，特别是在捕捉复杂时间模式以及缓解波动经济状况的影响方面。此外，本文讨论了集成梯度方法在模型可解释性方面的实现，并对预测结果所反映的经济动态提供了深入见解。