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A multi-scale forecasting model for CPI based on independent component analysis and non-linear autoregressive neural network

Author

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  • Liu, Jiayue
  • Ye, Jimin
  • E, Jianwei

Abstract

According to excellent performance on forecasting future trends based on historical observations, time series prediction has attracted considerable attention of many researchers. Consumer price index (CPI), as one of the indispensable indicators of macro economy, occupies a significant position in the national economic system. Arguably, the nonlinearity and nonstationarity of CPI series lead to poor accuracy of traditional prediction methods, from which the improvement of prediction performance is perceived as a challenging task. In this paper, a multi-scale forecasting technique for CPI is proposed, which is the hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), hierarchical agglomerative clustering (HAC), independent component analysis (ICA) and non-linear autoregressive (NAR) neural network. Firstly, the original time series is decomposed into several adaptive modes via the CEEMDAN. Secondly, HAC is introduced to cluster the modes according to their similarity aiming at performing dimensionality reduction. Thirdly, ICA is utilized to separate independent components (ICs) which implies the hidden information of time series. Finally, NAR neural network is applied to the ICs to obtain the forecasting sequences, and the final forecasting result is the combination of these individual forecasts. Comparative experiment results verify the superiority of the proposed hybrid methodology from the aspect of the performance criterion in CPI forecasting.

Suggested Citation

  • Liu, Jiayue & Ye, Jimin & E, Jianwei, 2023. "A multi-scale forecasting model for CPI based on independent component analysis and non-linear autoregressive neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s037843712200927x
    DOI: 10.1016/j.physa.2022.128369
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    References listed on IDEAS

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