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Adaptive Neuro-Fuzzy Inference System (ANFIS) Enhanced with Metaheuristic Optimization for Enhanced Time Series Forecasting

Authors: Gnanzou, D.

Published: 2025-05-02 12:00:45.692

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Abstract

Time series forecasting is a critical task across various domains, including finance, weather prediction, and supply chain management. However, the inherent non-linearity and non-stationarity of many real-world time series pose significant challenges for traditional forecasting models. This paper proposes an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized with metaheuristic algorithms for improved time series forecasting accuracy. Specifically, we investigate the integration of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize the parameters of the ANFIS architecture. The proposed hybrid approaches, ANFIS-PSO and ANFIS-GA, are compared against standard ANFIS and other benchmark time series forecasting methods using several real-world datasets. Experimental results demonstrate that the metaheuristic-optimized ANFIS models achieve significantly superior forecasting accuracy, particularly in handling complex and volatile time series data. The integration of metaheuristics enhances the adaptability and robustness of ANFIS, leading to more reliable and accurate predictions.

Keywords

ANFIS, Time Series Forecasting, Metaheuristic Optimization, Particle Swarm Optimization, Genetic Algorithm, Hybrid Systems, Adaptive Learning, Non-Stationary Data, Predictive Modeling, Fuzzy Logic

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How to Cite

Gnanzou, D., (2025-05-02 12:00:45.692). Adaptive Neuro-Fuzzy Inference System (ANFIS) Enhanced with Metaheuristic Optimization for Enhanced Time Series Forecasting. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 4.

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