Comparison of automated machine learning (AutoML) libraries in time series forecasting

Nagihan Akkurt; Servet Hasgül

Article Details

Comparison of automated machine learning (AutoML) libraries in time series forecasting

Authors:

Nagihan Akkurt | Servet Hasgül

Publisher:

Gazi University

Main Topics:

Mühendislik

Journal:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 3, pp.1693-1702

Document Type:

Article

Publication Date:

5/2024

Document Language:

Turkish

Document ID (Org No.):

2001310092

pISSN:

1300-1884

Keywords:

Abstract:

 Companies must make forecasts for the future to take necessary precautions, as well as to guard or expand their position and remain competitive. The development of data technologies has made it easier to reach meaningful data. Analyzing these data with methods such as artificial intelligence, machine learning, and deep learning makes it possible to obtain highly accurate results in future forecasts. However, the presence of numerous methods in the literature poses several challenges for researchers, including selecting the most suitable method and determining the appropriate techniques for model and hyper-parameter selection. Moreover, comparing different values in the model and making hyper-parameter selections can be tedious and time-consuming. Therefore, this study aims to use the Automated Machine Learning AutoML method, which is an advanced version of machine learning. AutoML automates machine learning models, allowing the use and development of machine learning algorithms without requiring expertise in this field. The study carried out forecasts using 6 different AutoML libraries on univariate time series datasets, and forecasting successes were compared over various performance metrics. According to the results obtained on the data set used, it was observed that the Auto_ARIMA library had the highest forecasting success rate among the selected libraries.

 Companies must make forecasts for the future to take necessary precautions, as well as to guard or expand their position and remain competitive. The development of data technologies has made it easier to reach meaningful data. Analyzing these data with methods such as artificial intelligence, machine learning, and deep learning makes it possible to obtain highly accurate results in future forecasts. However, the presence of numerous methods in the literature poses several challenges for researchers, including selecting the most suitable method and determining the appropriate techniques for model and hyper-parameter selection. Moreover, comparing different values in the model and making hyper-parameter selections can be tedious and time-consuming. Therefore, this study aims to use the Automated Machine Learning AutoML method, which is an advanced version of machine learning. AutoML automates machine learning models, allowing the use and development of machine learning algorithms without requiring expertise in this field. The study carried out forecasts using 6 different AutoML libraries on univariate time series datasets, and forecasting successes were compared over various performance metrics. According to the results obtained on the data set used, it was observed that the Auto_ARIMA library had the highest forecasting success rate among the selected libraries.

 Companies must make forecasts for the future to take necessary precautions, as well as to guard or expand their position and remain competitive. The development of data technologies has made it easier to reach meaningful data. Analyzing these data with methods such as artificial intelligence, machine learning, and deep learning makes it possible to obtain highly accurate results in future forecasts. However, the presence of numerous methods in the literature poses several challenges for researchers, including selecting the most suitable method and determining the appropriate techniques for model and hyper-parameter selection. Moreover, comparing different values in the model and making hyper-parameter selections can be tedious and time-consuming. Therefore, this study aims to use the Automated Machine Learning AutoML method, which is an advanced version of machine learning. AutoML automates machine learning models, allowing the use and development of machine learning algorithms without requiring expertise in this field. The study carried out forecasts using 6 different AutoML libraries on univariate time series datasets, and forecasting successes were compared over various performance metrics. According to the results obtained on the data set used, it was observed that the Auto_ARIMA library had the highest forecasting success rate among the selected libraries.

 Firmaların bulunduğu konumu koruması veya geliştirebilmesi, ileride meydana gelebilecek durumlara karşı önceden tedbir alabilmesi ve diğer firmalar ile rekabet edebilmesi açısından geleceğe yönelik tahminleme gerçekleştirmesi gereklidir. Gelişen veri teknolojileri ile anlamlı veriye ulaşabilmek kolaylaşmıştır ve yapay zekâ, makine öğrenmesi, derin öğrenme gibi yöntemlerle birlikte bu verilerin analiz edilmesiyle geleceğe yönelik tahminlemede oldukça başarılı sonuçlar elde edilebilmektedir. Ancak literatürde birçok farklı yöntemin bulunması, araştırmacıların hangi yöntemi kullanacağı, model ve hiper-parametre seçimi için hangi teknikleri kullanacağı gibi birçok problem oluşturabilmektedir. Model ve hiper-parametre seçimde farklı değerlerin karşılaştırılması zahmetli ve uzun süreli olabilir. Bu doğrultuda gerçekleştirilen çalışmanın amacı, makine öğrenmesinin gelişmiş bir versiyonu olan otomatikleştirilmiş makine öğrenmesi AutoML yöntemini kullanmaktır. AutoML, makine öğrenmesi modellerini otomatikleştirerek bu alanda uzmanlık gerektirmeden makine öğrenmesi algoritmalarının kullanımına ve geliştirilmesine olanak tanır. Çalışmada, tek değişkenli bir zaman serisi verisi üzerinde 6 farklı AutoML kütüphanesi ile tahminleme çalışması gerçekleştirilmiştir ve tahminleme başarıları çeşitli performans metrikleri üzerinden karşılaştırılmıştır. Kullanılan veri seti üzerinde elde edilen sonuçlara göre seçilen kütüphanelerden tahminleme başarısı en yüksek olanın Auto_ARIMA kütüphanesi olduğu gözlenmiştir.

Article Details

Comparison of automated machine learning (AutoML) libraries in time series forecasting

Authors:

Nagihan Akkurt | Servet Hasgül

Publisher:

Gazi University

Main Topics:

Mühendislik

Journal:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 3, pp.1693-1702

Document Type:

Article

Publication Date:

5/2024

Document Language:

Turkish

Document ID (Org No.):

2001310092

pISSN:

1300-1884

AutoML

AutoML libraries

Time series forecastingAutoML

AutoML kütüphaneleri

Zaman serisi tahminleme

Issues

Article Details

Comparison of automated machine learning (AutoML) libraries in time series forecasting

Authors: Nagihan Akkurt | Servet Hasgül

Publisher: Gazi University

Main Topics: Mühendislik

Journal: Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 3, pp.1693-1702

Document Type: Article

Publication Date: 5/2024

Document Language: Turkish

Document ID (Org No.): 2001310092

pISSN: 1300-1884

AutoML

AutoML libraries

Time series forecastingAutoML

AutoML kütüphaneleri

Zaman serisi tahminleme

Issues

Authors:

Nagihan Akkurt | Servet Hasgül

Publisher:

Gazi University

Main Topics:

Mühendislik

Journal:

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi Vol.39 Issue 3, pp.1693-1702

Document Type:

Article

Publication Date:

5/2024

Document Language:

Turkish

Document ID (Org No.):

2001310092

pISSN:

1300-1884