On Performance of ABC, FPA, BBO and MVO Algorithms in ANFIS Training for Short-Term Forecasting of Crude Oil Price

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Abstract
Crude oil is one of the most important assets that are used in the production of many industrial products in a wide variety of areas. The importance of crude oil has made it important to predict its future price. Therefore, it is possible to come across many studies in the literature in which the price of crude oil is estimated in the short or long term. In this study, innovative adaptive neuro-fuzzy inference systems (ANFIS) based approaches are proposed to estimate the daily minimum and maximum prices of crude oil. The used data was taken from the period between January 3, 2022, and December 29, 2023. A total of 516 different days of data were collected to create the dataset for analysis. For daily forecasting, time series data were transformed into a data set consisting of two inputs and one output. Moth-flame optimization algorithm (MFO), flower pollination algorithm (FPA), biogeography-based optimization (BBO) and artificial bee colony (ABC) were used in training ANFIS. The results obtained in the training and testing processes were compared. When the results obtained were compared, it was shown that the relevant algorithms were effective in the daily estimation of crude oil. It has been observed that effective results are also achieved at low evaluation numbers, especially thanks to the fast convergence feature of the MFO and BBO algorithms.

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