IDEAS home Printed from https://ideas.repec.org/p/gii/giihei/heidwp05-2025.html

Forecasting Banking System Liquidity Using Payment System Data in Uzbekistan

Author

Listed:
  • Shakhzod Abdullaevich Makhmudov

    (The Central Bank of Uzbekistan)

Abstract

Forecasting banking system liquidity is crucial for the e ective monetary policy implementation. This study investigates the e ectiveness of various econometric and machine learning models in predicting the autonomous factors of banking system liquidity. The research compares widely used econometric models such as SARIMA, Exponential Smoothing, and Prophet alongside ma- chine learning models like Random Forest, applying various preprocessing techniques, including power transformations, scaling, and trend-cycle decomposition. Moreover, ensemble methods, like weighted blending and stacking, were used to improve accuracy. Experimental results in- dicate that SARIMA was the best individual model, but ensemble with Prophet and Random Forest further improved forecast performance. Neural network models underperformed poten- tially due to challenges in optimizing their architectures. Future research intends to explore multivariate and structural models, as well as advanced neural architectures, to enhance pre- dictive accuracy.

Suggested Citation

  • Shakhzod Abdullaevich Makhmudov, 2025. "Forecasting Banking System Liquidity Using Payment System Data in Uzbekistan," IHEID Working Papers 05-2025, Economics Section, The Graduate Institute of International Studies, revised 17 Feb 2025.
    • Handle: RePEc:gii:giihei:heidwp05-2025
    as

    Download full text from publisher

    File URL: http://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP05-2025.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Markus Burger & Bernhard Klar & Alfred Muller & Gero Schindlmayr, 2004. "A spot market model for pricing derivatives in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 4(1), pages 109-122.
    3. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    4. Jacinta Chan Phooi M’ng & Mohammadali Mehralizadeh, 2016. "Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
    5. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    6. Philippe C. Besse & Herve Cardot & David B. Stephenson, 2000. "Autoregressive Forecasting of Some Functional Climatic Variations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 673-687, December.
    7. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    8. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    9. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    10. Zhiqiang Guo & Huaiqing Wang & Quan Liu & Jie Yang, 2014. "A Feature Fusion Based Forecasting Model for Financial Time Series," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
    11. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    12. Hans Weytjens & Enrico Lohmann & Martin Kleinsteuber, 2021. "Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet," Electronic Commerce Research, Springer, vol. 21(2), pages 371-391, June.
    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    3. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    4. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    5. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    6. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    7. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    8. Oikonomou, Konstantinos & Damigos, Dimitris & Dimitriou, Dimitrios, 2025. "Globality in the metal markets: Leveraging cross-learning to forecast aluminum and copper prices," Resources Policy, Elsevier, vol. 103(C).
    9. McWilliams, William N. & Isengildina Massa, Olga & Stewart, Shamar L., 2024. "Annual Food Price Inflation Forecasting: A Macroeconomic Random Forest Approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343923, Agricultural and Applied Economics Association.
    10. Manuela Royer-Carenzi & Hossein Hassani, 2025. "Deviations from Normality in Autocorrelation Functions and Their Implications for MA(q) Modeling," Stats, MDPI, vol. 8(1), pages 1-37, February.
    11. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    12. Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar, 2025. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," International Journal of Forecasting, Elsevier, vol. 41(3), pages 953-981.
    13. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    14. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    15. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    16. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    17. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    18. Abdelfatah, Omar Sharafeldin Mohamed, 2026. "Machine Learning Approaches for Improving Demand Forecasting Accuracy in Retail Supply Chains," SocArXiv 4z9be_v1, Center for Open Science.
    19. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    20. Dag Tjøstheim & Martin Jullum & Anders Løland, 2023. "Some recent trends in embeddings of time series and dynamic networks," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 686-709, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Systems; Standards; Regimes; Government and the Monetary System

    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:gii:giihei:heidwp05-2025. 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: Dorina Dobre (email available below). General contact details of provider: https://edirc.repec.org/data/ieheich.html .

    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.