IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i1p200-215.html

Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model

Author

Listed:
  • Yun Zhou
  • Xuxu Zhu

Abstract

Because the exchange rate is essentially a dynamic and nonlinear system, exchange rate forecasting has been one of the most challenging topics in the financial field. This paper proposes a novel idea of “decomposition‐reconstruction‐integration” to predict exchange rate. First, based on ICEEMDAN, the original sequences are decomposed into multifrequency IMFs. Second, we use t‐test to determine the high‐frequency IMFs, low‐frequency IMFs, and trend sequence and reconstruct the high‐frequency IMFs into a new component sequence. Third, we use CNN‐LSTM model to predict these components separately and finally get the final prediction result by integration. This paper takes the USD/RMB exchange rate as research object, and the experimental results show that (1) the fluctuations of USD/RMB exchange rate are mainly affected by the trend sequence and low‐frequency IMFs and are less affected by high‐frequency IMFs. (2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN‐CNN‐LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. In summary, this paper has certain theoretical significance and application value.

Suggested Citation

  • Yun Zhou & Xuxu Zhu, 2025. "Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 200-215, January.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:1:p:200-215
    DOI: 10.1002/for.3190
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3190
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3190?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
    ---><---

    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. Huseyin Ince & Ali Fehim Cebeci & Salih Zeki Imamoglu, 2019. "An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 817-831, February.
    3. Raymond A.K. Cox & Grace W.-Y. Wang, 2014. "Predicting the US bank failure: A discriminant analysis," Economic Analysis and Policy, Elsevier, vol. 44(2), pages 202-211.
    4. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    5. Jichang Dong & Wei Dai & Ling Tang & Lean Yu, 2019. "Why do EMD‐based methods improve prediction? A multiscale complexity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 714-731, November.
    6. Yildirim, Zekeriya & Arifli, Arif, 2021. "Oil price shocks, exchange rate and macroeconomic fluctuations in a small oil-exporting economy," Energy, Elsevier, vol. 219(C).
    7. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2020. "Incorporating the RMB internationalization effect into its exchange rate volatility forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Weiqian Zhang & Songsong Li & Zhichang Guo & Yizhe Yang, 2023. "A hybrid forecasting model based on deep learning feature extraction and statistical arbitrage methods for stock trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1729-1749, November.
    9. Wada, Tatsuma, 2022. "Out-of-sample forecasting of foreign exchange rates: The band spectral regression and LASSO," Journal of International Money and Finance, Elsevier, vol. 128(C).
    10. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
    11. Hualing Lin & Qiubi Sun & Sheng-Qun Chen, 2020. "Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    12. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    13. Chaker Aloui & Besma Hkiri & Shawkat Hammoudeh & Muhammad Shahbaz, 2018. "A Multiple and Partial Wavelet Analysis of the Oil Price, Inflation, Exchange Rate, and Economic Growth Nexus in Saudi Arabia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 935-956, March.
    14. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    15. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    16. Steve C Nwankwo, 2014. "Autoregressive Integrated Moving Average (ARIMA) Model for Exchange Rate (Naira to Dollar)," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 3, July.
    17. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    18. Ahmad Baharumshah & Venus Liew, 2006. "Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models," Open Economies Review, Springer, vol. 17(2), pages 235-251, April.
    19. Muhammad Akbar, 2023. "Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1470-1487, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dinggao Liu & Robert 'Slepaczuk & Zhenpeng Tang, 2025. "EXFormer: A Multi-Scale Trend-Aware Transformer with Dynamic Variable Selection for Foreign Exchange Returns Prediction," Papers 2512.12727, arXiv.org, revised Jan 2026.

    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. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    2. 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.
    3. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    4. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    5. Hassan Oukhouya & Aziz Lmakri & Mohamed El Yahyaoui & Raby Guerbaz & Said El Melhaoui & Moustapha Faizi & Khalid El Himdi, 2025. "Predictive modeling for the Moroccan financial market: a nonlinear time series and deep learning approach," Future Business Journal, Springer, vol. 11(1), pages 1-19, December.
    6. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    7. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    8. Degiannakis, Stavros & Kafousaki, Eleftheria, 2025. "Disaggregating VIX," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1559-1588.
    9. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    10. Xu, Kunliang & Niu, Hongli, 2022. "Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Gong, Jue & Li, Zhao-Chen & Zhu, You, 2024. "Who dominate the information flowing between innovative and traditional financial assets? A multiscale entropy-based approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 329-358.
    12. Bai, Yun & Yan, Chuanmiao & Jiang, Fuxin & Wei, Yunjie & Wang, Shouyang, 2026. "Exchange rate forecasting with macroeconomic data: Evidence from a novel comprehensive ensemble approach," Journal of International Money and Finance, Elsevier, vol. 160(C).
    13. Zhe Jiang & Zili Zhang & Lin Zhang, 2025. "Forecasting China’s Short-Term Energy Futures Price Using a Novel Secondary Decomposition-Optimized System," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 4009-4043, November.
    14. Chandrarin, Grahita & Sohag, Kazi & Cahyaningsih, Diyah Sukanti & Yuniawan, Dani & Herdhayinta, Heyvon, 2022. "The response of exchange rate to coal price, palm oil price, and inflation in Indonesia: Tail dependence analysis," Resources Policy, Elsevier, vol. 77(C).
    15. Zefan Dong & Yonghui Zhou, 2024. "A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM," Mathematics, MDPI, vol. 12(16), pages 1-16, August.
    16. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    17. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    18. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
    19. Li Xiangfei & Zhang Zaisheng & Huang Chao, 2014. "An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 2(6), pages 481-504, December.
    20. Qing Qin & Lingxiao Li, 2025. "A VMD-Based Four-Stage Hybrid Forecasting Model with Error Correction for Complex Coal Price Series," Mathematics, MDPI, vol. 13(18), pages 1-24, September.

    More about this item

    Statistics

    Access and download statistics

    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:wly:jforec:v:44:y:2025:i:1:p:200-215. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.