IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v609y2023ics037843712200927x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712200927X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128369?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    2. Cogoljević, Dušan & Gavrilović, Milan & Roganović, Miloš & Matić, Ivana & Piljan, Ivan, 2018. "Analyzing of consumer price index influence on inflation by multiple linear regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 941-944.
    3. E, Jianwei & Bao, Yanling & Ye, Jimin, 2017. "Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 412-427.
    4. Xiaolu Zhang & Zeshui Xu, 2015. "Hesitant fuzzy agglomerative hierarchical clustering algorithms," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 562-576, February.
    5. Liu, Guofang & Fang, Xi & Huang, Yuan & Zhao, Weidong, 2021. "Identifying the role of consumer and producer price index announcements in stock index futures price changes," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 87-101.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xuerui & Li, Xiangyu & Li, Shaoting, 2022. "Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm," Applied Energy, Elsevier, vol. 328(C).
    2. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    3. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    4. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    5. Hosseini, Seyed Hossein & Shakouri G., Hamed & Kazemi, Aliyeh, 2021. "Oil price future regarding unconventional oil production and its near-term deployment: A system dynamics approach," Energy, Elsevier, vol. 222(C).
    6. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    7. Nacira Agram & Bernt Øksendal & Jan Rems, 2024. "Deep learning for quadratic hedging in incomplete jump market," Digital Finance, Springer, vol. 6(3), pages 463-499, September.
    8. Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    9. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    10. Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    12. Zeyu Zhang & Xiaoqian Liu & Xiling Zhang & Zhishan Yang & Jian Yao, 2024. "Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage," Energies, MDPI, vol. 17(17), pages 1-22, August.
    13. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    14. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    15. Hloušková, Z. & Ženíšková, P. & Prášilová, M., 2018. "Comparison of Agricultural Costs Prediction Approaches," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(01).
    16. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
    17. Axelsson, Birger & Song, Han-Suck, 2023. "Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model," Working Paper Series 23/10, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, revised 14 Nov 2023.
    18. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
    19. Danijel Jevtic & Romain Deleze & Joerg Osterrieder, 2022. "AI for trading strategies," Papers 2208.07168, arXiv.org.
    20. Qing Zhu & Renxian Zuo & Shan Liu & Fan Zhang, 2020. "Online dynamic group-buying community analysis based on high frequency time series simulation," Electronic Commerce Research, Springer, vol. 20(1), pages 81-118, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s037843712200927x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.