## 1 Introduction
### Rationale and Structure  
The rationale behind the structure of our review is based on organizing the collected scientific literature first in relation to the machine learning model being compared with ARIMA and second in relation to the field of forecasting applications. In that scope, the first model category to be presented in detail is ARIMA, which is the center of our review, and then we organize the machine learning approaches by model category. For each of the categories, we present the theoretical background, and then we demonstrate the relevant scientific literature organized by application category. The same principle applies to the category of hybrid forecasting models, meaning the combination of ARIMA and machine learning algorithms. Finally, we evaluate the reviewed scientific literature in total, and based on the finding that machine learning models outperform the ARIMA approach in the majority of forecasting applications, we aim to uncover the conditions under which we have a superior ARIMA forecasting result.  
At this point, it is important to note that our work does not propose a new time series forecasting model, but rather constitutes an organized review of the published scientific literature comparing the ARIMA approach to different machine learning, and hybrid prediction models. We consider this aspect of our work crucial to the feasibility of our study because we aim to compare the optimized ARIMA and machine learning models on the same datasets. Therefore, although the scientific literature applying individual ARIMA or machine learning models to forecasting problems is extensive, we base our study only on the explicit comparisons of the three categories of models (ARIMA, machine learning, and hybrid).  
The present work is organized as follows: The problem of time series forecasting, which--along with the ARIMA technique--constitutes the center of our work, is presented in Section 2, along with the morphological characteristics of the time series that affect the choice of the forecasting algorithm and their respective results. A review is also carried out regarding the time series data sources and their inherent characteristics that predetermine the format of the data to be analyzed and, consequently, the choice of the forecasting algorithms. In Sections 3 and 4, we perform a theoretical review of the algorithms and machine learning models that will be presented in the context of the attempted comparisons, and subsequently, we present the relevant scientific literature, organized by field of research (financial sector, medical care, etc.). Specifically, in Section 3.1, we refer to the comparison of the SVM model with the classic ARIMA statistical technique, while in Section 3.2, the theory and applications of decision tree-based models are analyzed. Section 4 presents the rationale behind the development of hybrid forecasting models with a review of the indicative scientific literature. In any case, the hybrid models chosen for presentation and analysis are a combination of the ARIMA technique with a specific machine learning model. Section 5 constitutes a practical evaluation of the reviewed literature, with a focus on extracting the conditions under which the ARIMA approach exhibits superior predictive performance compared with the machine learning models. Finally, Section 6 covers the conclusions of our review.