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

Ebubekir Kaya; Ahmet Kaya; Eyüp Sıramkaya; Ceren Baştemur Kaya

Makale detayları

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

Yazarlar:

Ebubekir Kaya | Ahmet Kaya | Eyüp Sıramkaya | Ceren Baştemur Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Fuzzy Computation | Planning and Decision Making | Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.314-330

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001343818

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Ebubekir Kaya | Ahmet Kaya | Eyüp Sıramkaya | Ceren Baştemur Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Fuzzy Computation | Planning and Decision Making | Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.314-330

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001343818

pISSN:

2147-3129

Artificial Intelligence

ANFIS

Crude Oil

Metaheuristic Algorithms

Time Series Analysis

Sayı

Makale detayları

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

Yazarlar: Ebubekir Kaya | Ahmet Kaya | Eyüp Sıramkaya | Ceren Baştemur Kaya

Yayıncı: Bitlis Eren University

Ana Başlıklar: Fuzzy Computation | Planning and Decision Making | Artificial Intelligence (Other)

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.314-330

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001343818

pISSN: 2147-3129

Artificial Intelligence

ANFIS

Crude Oil

Metaheuristic Algorithms

Time Series Analysis

Sayı

Yazarlar:

Ebubekir Kaya | Ahmet Kaya | Eyüp Sıramkaya | Ceren Baştemur Kaya

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Fuzzy Computation | Planning and Decision Making | Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.314-330

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001343818

pISSN:

2147-3129