## 3 Machine Learning Models
### Weather and Environmental Parameter Studies  
Another popular field of forecasting in the scientific literature is weather forecasting, which is a difficult task due to the chaotic nature of atmospheric variations. The work of [PERSON] et al. (2018) compares classical ARIMA with deep learning models, aiming to derive temperature predictions. The used deep learning model consists of one-dimensional convolutional layers to extract the spatial features of the data, and layers based on the LSTM architecture to extract the temporal features. The two models are applied to the analysis of temperature data from the Szeged area in Hungary, with an hourly sampling frequency. The experimental results clearly indicate that the deep learning model has a superior performance in terms of accuracy over the ARIMA model as it was able to achieve a lower root mean square error value. The MSE obtained by the deep learning model is 21% lower than that of the ARIMA model. In the work of [PERSON] et al. (2018), ARIMA, Prophet, and LSTM models are compared in predicting the Air Quality Index (AQI) values for the city of Delhi, India. According to the survey results, the Prophet model performs the best in terms of the mean absolute percentage error (MAPE) metric; however, it is greatly outperformed by the other two models in the remaining metrics, namely the root mean square error (RMS) and mean absolute error (MAE). In addition, it appears that the LSTM model outperforms the ARIMA model in terms of all three metrics and therefore can be considered the best-performing model for this data set. In a slightly different field of research, the work of [PERSON] et al. (2018) deals with the forecasting of time series consisting of measured wind speeds in coastal areas of Scotland with an hourly sampling frequency. The data sets used have been collected using a special setup with three measuring positions. The models compared in terms of time series forecasting ability were seasonal ARIMA (SARIMA) and deep learning models GRU and LSTM. To maximize performance, both SARIMA and deep learning models tuned their hyperparameters through a combination of manual search and grid search. Based on the six research metrics used, the SARIMA approach outperformed deep learning-based algorithms in prediction performance. 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. Furthermore, the SARIMA model requires the setting of only six parameters (p, q, d, P, Q, D), while many more hyperparameters need to be evaluated in GRU and LSTM, such as the number of nodes in each layer, the number of layers, the size of the input, the number of training epochs, the optimizer, the chosen activation function, the initialization of the kernel function, and so on. Although, in recent decades, much attention has been paid to the development of suitable models to achieve accurate offshore wind speed forecasting, conventional neural networks prove to be deficient in producing short-term time series forecasts. These models show problems of overfitting or \"entrapment\" at local extremes. The SARIMA approach is a simpler and more efficient tool than deep learning-based models for predicting offshore wind speed time series. Another example of environmental time series forecasting is the work of [PERSON] et al. (2018), where the LSTM algorithm is compared with classical ARIMA in terms of predicting carbon dioxide levels in the port area of Igoumenitsa. A batch size of 100, 1000, and 7000 was used for training, and the performance of the models was evaluated using the RMSE and MAE metrics. It is shown through the analysis results that for a batch size of 7000, the LSTM model gives a good prediction result based on the RMSE and MAE metrics in terms of model training and validation losses.