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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...

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Detalles Bibliográficos
Autores principales: Zhang, Junhuan, Wen, Jiaqi, Yang, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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