IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i1d10.1007_s10614-024-10723-0.html
   My bibliography  Save this article

Cash Flow Forecasting for Self-employed Workers: Fuzzy Inference Systems or Parametric Models?

Author

Listed:
  • Luis Palomero

    (Declarando Asesores 3.0 S.l.
    Institute of New Imaging Technologies, Universitat Jaume I)

  • Vicente García

    (Universidad Autónoma de Ciudad Juárez)

  • J. Salvador Sánchez

    (Institute of New Imaging Technologies, Universitat Jaume I)

Abstract

Cash flow forecasting is an important task for any organization, but it becomes crucial for self-employed workers. In this paper, we model the cash flow of three real self-employed workers as a time series problem and compare the performance of conventional parametric methods against two types of fuzzy inference systems in terms of both prediction error and processing time. Our evaluation demonstrates that there is no winning model, but that each forecasting method’s performance depends on the characteristics of the cash flow data. However, experimental results suggest that parametric methods and Mamdani-type fuzzy inference systems outperform Takagi–Sugeno–Kang-type systems.

Suggested Citation

  • Luis Palomero & Vicente García & J. Salvador Sánchez, 2025. "Cash Flow Forecasting for Self-employed Workers: Fuzzy Inference Systems or Parametric Models?," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 645-679, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10723-0
    DOI: 10.1007/s10614-024-10723-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10723-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10723-0?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Ly, Kim Tien, 2021. "A COVID-19 forecasting system using adaptive neuro-fuzzy inference," Finance Research Letters, Elsevier, vol. 41(C).
    3. Zuhaimy Ismail & Riswan Efendi & Mustafa Mat Deris, 2015. "Application of Fuzzy Time Series Approach in Electric Load Forecasting," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 11(03), pages 229-248.
    4. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    5. 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).
    6. Tangsucheeva, Rattachut & Prabhu, Vittaldas, 2014. "Stochastic financial analytics for cash flow forecasting," International Journal of Production Economics, Elsevier, vol. 158(C), pages 65-76.
    7. Jaehong Lee & Eunsoo Kim, 2019. "Foreign Monitoring and Predictability of Future Cash Flow," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    8. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    9. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    12. Dechow, Patricia M. & Kothari, S. P. & L. Watts, Ross, 1998. "The relation between earnings and cash flows," Journal of Accounting and Economics, Elsevier, vol. 25(2), pages 133-168, May.
    13. Haixiang Yao & Xun Li & Zhifeng Hao & Yong Li, 2016. "Dynamic asset–liability management in a Markov market with stochastic cash flows," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1575-1597, October.
    14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    15. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    16. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
    17. Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
    18. Edgar Lorenzo-Sáez & Eloína Coll-Aliaga & Jose-Vicente Oliver-Villanueva & Fernando Prieto del Campo & Victoria Lerma-Arce, 2022. "Analysis of the COVID-19 Lockdown’s Impact on Air Quality in the Larger Cities of Spain," Sustainability, MDPI, vol. 14(9), pages 1-9, May.
    19. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    20. 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. Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
    3. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    4. UÄŸur Åžener & Salvatore Joseph Terregrossa, 2024. "A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts," SAGE Open, , vol. 14(3), pages 21582440241, August.
    5. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    6. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    7. 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.
    8. 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).
    9. d'Aspremont, Alexandre & Ben Arous, Simon & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2025. "Satellites turn “concrete”: Tracking cement with satellite data and neural networks," Journal of Econometrics, Elsevier, vol. 249(PC).
    10. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    11. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    12. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    13. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    14. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    15. Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
    16. Bloznelis, Daumantas, 2017. "Hedging under square loss," MPRA Paper 83442, University Library of Munich, Germany.
    17. Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2024. "Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data," Working Papers 202450, University of Pretoria, Department of Economics.
    18. Isabel Figuerola‐Ferretti & Alejandro Rodríguez & Eduardo Schwartz, 2021. "Oil price analysts' forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1351-1374, September.
    19. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    20. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10723-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.