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China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model
This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China’s Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different ti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205526/ https://www.ncbi.nlm.nih.gov/pubmed/35714074 http://dx.doi.org/10.1371/journal.pone.0269529 |
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author | Zhang, Junhuan Wen, Jiaqi Yang, Zhen |
author_facet | Zhang, Junhuan Wen, Jiaqi Yang, Zhen |
author_sort | Zhang, Junhuan |
collection | PubMed |
description | This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China’s Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency. |
format | Online Article Text |
id | pubmed-9205526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92055262022-06-18 China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model Zhang, Junhuan Wen, Jiaqi Yang, Zhen PLoS One Research Article This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China’s Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency. Public Library of Science 2022-06-17 /pmc/articles/PMC9205526/ /pubmed/35714074 http://dx.doi.org/10.1371/journal.pone.0269529 Text en © 2022 Zhang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhang, Junhuan Wen, Jiaqi Yang, Zhen China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title | China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title_full | China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title_fullStr | China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title_full_unstemmed | China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title_short | China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model |
title_sort | china’s gdp forecasting using long short term memory recurrent neural network and hidden markov model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205526/ https://www.ncbi.nlm.nih.gov/pubmed/35714074 http://dx.doi.org/10.1371/journal.pone.0269529 |
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