## 5 Discussion and Practical Evaluation
(2018), according to which machine learning methods improve their relative predictive performance over classical prediction algorithms as the data sample size grows. In the work of [PERSON] et al. (2018), ARIMA is quite effective relative to LSTMs in making bitcoin price predictions in the short term, but as time goes on, it shows a decreasing rate of accuracy (Paragraph \"Financial Data\"). In the work of [PERSON] et al. (2018), the authors attempt to predict the rates of confirmed cases, recovered patients, and deaths from the COVID-19 disease in different countries of the world. For most cross-country time series data, the deep learning-based models (LSTM and GRU) outperform the statistical ARIMA and SARIMA models, with RMSE values 40 times smaller than those of the ARIMA models. However, in some countries, statistical models outperformed deep learning models (Paragraph \"Healthcare Data\"). Due to the highly dynamic nature of disease-specific data, the information they contain depends on the country of origin as well as the time at which they were generated. The shape of the data coming from some countries is non-linear, while ARIMA models are shown to perform better in modeling data that follows linear relationships. On the other hand, RNN models performed better in countries whose data were non-linear. These results once again confirm the conclusion made above, namely that since ARIMA is much more robust and efficient in linear time series analysis, its choice over machine learning algorithms should primarily depend on the linearity of the data. In this work, it is also interesting that while ARIMA is, according to the results, an ideal choice for modeling the rates of confirmed cases and recovered patients for some countries whose data have the appropriate profile, its performance is nevertheless very poor for modeling the number of deaths from COVID-19 in all countries, which is likely related to the increasing complexity of the data and the conditions that lead to the death of patients in general. In the work of [PERSON] et al. [54], which concerns the prediction of time series consisting of the measured wind speed in coastal areas, the SARIMA approach outperformed deep learning-based algorithms in prediction performance (Paragraph \"Weather and Environmental Parameter Studies\"). The authors of this paper argue that the SARIMA approach is more suitable for dealing with offshore wind speed forecasting because of its ability to directly support making predictions of seasonal elements on univariate datasets. Finally, in the work of [PERSON] et al. [17], ARIMA again outperforms LSTM models in predicting harbor area CO2 levels, which is likely related to the nature of the forecast data (Paragraph \"Weather and Environmental Parameter Studies\"). Hybrid Models over ARIMA  
On the other hand, as far as the hybrid forecasting models are concerned, in any case and in the context of this work, they result in better forecasting performance compared with the single ARIMA model, as they combine modeling features both from the point of view of classical statistical algorithms and on the part of machine learning models, making them capable of dealing with predictive data at multiple levels of analysis. Final Remarks  
There are various conclusions derived from the scientific literature cited in the previous paragraphs. At first, we observe that the optimal choice of a forecasting algorithm can be different, for different versions of a dataset (e.g., different geographic regions in drought forecasting [24], different countries in modeling rates of confirmed and recovered cases of COVID-19 [50]) within the same forecasting task. The particular differences in the datasets' nature, also inferred from the optimal model choice, can be of great value in a multitude of forecasting and modeling tasks and can be an important source of information for region characterization. On the other hand, in some applications, it is the dataset characteristics inherent to the specific data driven network that drive the modeling choice.