Work

Baltic dry index forecasting using a neuro-fuzzy inference system

Year: 2025

Type: article

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).

Source: Journal of Economics and Finance

DOI: 10.1007/s12197-025-09720-2

Authors: Ioanna Atsalaki, George S. Atsalakis, Konstantinos D. Melas, Nektarios A. Michail

Institutions: Technical University of Crete, University of Macedonia, Metropolitan College, University of Western Macedonia, Central Bank of Cyprus

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funders: Hellenic Academic Libraries Link, University of Western Macedonia