Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization
Macroeconomic policy optimization is essential in today’s complex economic environments, yet existing models often struggle to effectively utilize high-frequency IoT data alongside traditional low-frequency indicators, limiting responsiveness to rapid changes. To address this, we propose MLD-Net, a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000924 |
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| author | Cong Peng Yongshan Zhang Liheng Jiang |
| author_facet | Cong Peng Yongshan Zhang Liheng Jiang |
| author_sort | Cong Peng |
| collection | DOAJ |
| description | Macroeconomic policy optimization is essential in today’s complex economic environments, yet existing models often struggle to effectively utilize high-frequency IoT data alongside traditional low-frequency indicators, limiting responsiveness to rapid changes. To address this, we propose MLD-Net, a framework that combines IoT high-frequency data with economic data through MIDAS regression, LSTM networks for temporal dynamics, and Deep Q-Networks (DQN) for reinforcement learning-based policy optimization. MLD-Net effectively aligns multi-frequency data, captures complex temporal patterns, and adjusts policies in real-time. Experimental results show that MLD-Net performs well in predicting key macroeconomic indicators, such as GDP and inflation, adapting effectively to dynamic economic conditions. Further analysis demonstrates that each component—MIDAS, LSTM, and DQN—enhances the model’s predictive power, highlighting the benefits of integrating these techniques within one framework. In conclusion, MLD-Net provides a valuable tool for more responsive macroeconomic policy-making, advancing the integration of real-time data for adaptive economic decisions. |
| format | Article |
| id | doaj-art-7b6e0fcf3dc84af399d9cab990405c2b |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-7b6e0fcf3dc84af399d9cab990405c2b2025-02-06T05:11:11ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119222231Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimizationCong Peng0Yongshan Zhang1Liheng Jiang2School of Economics and Finance, Guizhou University of Commerce, Guiyang, Guizhou, 550014, ChinaEconomics Management College, Weifang University of Science and Technology, Jinguang Street, Shouguang, 262700, Shandong, China; Corresponding author.CVS Health Corporation, RI, 02895, USAMacroeconomic policy optimization is essential in today’s complex economic environments, yet existing models often struggle to effectively utilize high-frequency IoT data alongside traditional low-frequency indicators, limiting responsiveness to rapid changes. To address this, we propose MLD-Net, a framework that combines IoT high-frequency data with economic data through MIDAS regression, LSTM networks for temporal dynamics, and Deep Q-Networks (DQN) for reinforcement learning-based policy optimization. MLD-Net effectively aligns multi-frequency data, captures complex temporal patterns, and adjusts policies in real-time. Experimental results show that MLD-Net performs well in predicting key macroeconomic indicators, such as GDP and inflation, adapting effectively to dynamic economic conditions. Further analysis demonstrates that each component—MIDAS, LSTM, and DQN—enhances the model’s predictive power, highlighting the benefits of integrating these techniques within one framework. In conclusion, MLD-Net provides a valuable tool for more responsive macroeconomic policy-making, advancing the integration of real-time data for adaptive economic decisions.http://www.sciencedirect.com/science/article/pii/S1110016825000924Macroeconomic policy optimizationIoT dataMixed-frequency dataMIDAS regressionLSTM networksDeep Q-network |
| spellingShingle | Cong Peng Yongshan Zhang Liheng Jiang Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization Alexandria Engineering Journal Macroeconomic policy optimization IoT data Mixed-frequency data MIDAS regression LSTM networks Deep Q-network |
| title | Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization |
| title_full | Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization |
| title_fullStr | Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization |
| title_full_unstemmed | Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization |
| title_short | Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization |
| title_sort | integrating iot data and reinforcement learning for adaptive macroeconomic policy optimization |
| topic | Macroeconomic policy optimization IoT data Mixed-frequency data MIDAS regression LSTM networks Deep Q-network |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825000924 |
| work_keys_str_mv | AT congpeng integratingiotdataandreinforcementlearningforadaptivemacroeconomicpolicyoptimization AT yongshanzhang integratingiotdataandreinforcementlearningforadaptivemacroeconomicpolicyoptimization AT lihengjiang integratingiotdataandreinforcementlearningforadaptivemacroeconomicpolicyoptimization |