HMM-LSTM Fusion Model for Economic Forecasting

TL;DR

This paper proposes a novel fusion of Hidden Markov Models and LSTM neural networks to improve the accuracy and interpretability of economic forecasting, specifically CPI inflation rates.

Contribution

It introduces a new approach that integrates HMM-derived hidden states into LSTM models, enhancing predictive performance and interpretability in economic forecasting.

Findings

HMM-LSTM fusion improves forecasting accuracy.

Incorporating HMM states captures complex economic patterns.

Model interpretability is enhanced through Integrated Gradients.

Abstract

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.