China's GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model.

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  • Author(s): Zhang J;Zhang J;Zhang J; Wen J; Wen J; Yang Z; Yang Z
  • Source:
    PloS one [PLoS One] 2022 Jun 17; Vol. 17 (6), pp. e0269529. Date of Electronic Publication: 2022 Jun 17 (Print Publication: 2022).
  • Publication Type:
    Journal Article; Research Support, Non-U.S. Gov't
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Memory, Short-Term* ; Neural Networks, Computer*; China ; Forecasting ; Gross Domestic Product
    • Abstract:
      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.
      Competing Interests: The authors have declared that no competing interests exist.
    • Publication Date:
      Date Created: 20220617 Date Completed: 20220621 Latest Revision: 20220716
    • Publication Date:
      20240105
    • Accession Number:
      PMC9205526
    • Accession Number:
      10.1371/journal.pone.0269529
    • Accession Number:
      35714074