arXiv 1 Jan 2025 · Machine Learning
arXiv:2501.02002 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey and Imbens (2019) Machine Learning Methods Economists Should Know About | 0.511 | 2 | 1 | 100% |
| 2 | Zheng, Xu and Xiao (2023) Deep learning in economics: a systematic and critical review | 0.511 | 2 | 1 | 100% |
| 3 | Zhang, Wen and Yang (2022) China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model | 0.511 | 2 | 1 | 100% |
| 4 | Yang, Zheng and E (2020) Interpretable Neural Networks for Panel Data Analysis in Economics | 0.511 | 2 | 1 | 100% |
| 5 | Siami-Namini and Namin (2018) Forecasting Economics and Financial Time Series: ARIMA vs. LSTM | 0.511 | 2 | 1 | 100% |
| 6 | Dickey and Fuller (1979) Distribution of the Estimators for Autoregressive Time Series With a Unit Root | 0.405 | 1 | 1 | 100% |
| 7 | Thorbecke (2002) A Dual Mandate for the Federal Reserve: The Pursuit of Price Stability and Full Employment | 0.405 | 1 | 1 | 100% |
| 8 | Dempster, Laird and Rubin (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm | 0.405 | 1 | 1 | 100% |
| 9 | St.$\:$Louis$\:$Fed (1991) Federal Reserve Economic Data | 0.405 | 1 | 1 | 100% |
| 10 | Sivakumar (2024) HMM-RNN Fusion Economic Forecasting self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.