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

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 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.