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Baltic dry index forecasting using a neuro-fuzzy inference system

Author

Listed:
  • Ioanna Atsalaki

    (Technical University of Crete)

  • George S. Atsalakis

    (Technical University of Crete)

  • Konstantinos D. Melas

    (University of Western Macedonia
    Metropolitan College
    University of Macedonia)

  • Nektarios A. Michail

    (Central Bank of Cyprus)

Abstract

We employ a Fuzzy Inference System, with a specific focus on utilizing a hybrid intelligent system known as ANFIS (Adaptive Neuro Fuzzy Inference System) to forecast the Baltic Dry Index. This system integrates the adaptive learning features of neural networks with the logical reasoning of fuzzy logic, thereby offering superior forecasting accuracy compared to single-method approaches. Our findings demonstrate the superior performance of the ANFIS model in comparison to a feed-forward neural network and two traditional models, namely AR (Autoregressive) and ARMA (Autoregressive Moving Average), in terms of Root Mean Squared Error (RMSE).

Suggested Citation

  • Ioanna Atsalaki & George S. Atsalakis & Konstantinos D. Melas & Nektarios A. Michail, 2025. "Baltic dry index forecasting using a neuro-fuzzy inference system," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(3), pages 682-709, September.
    • Handle: RePEc:spr:jecfin:v:49:y:2025:i:3:d:10.1007_s12197-025-09720-2
    DOI: 10.1007/s12197-025-09720-2
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    JEL classification:

    • F1 - International Economics - - Trade
    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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