## 4 Description of the Data Set  
The data set used in this study for the LSTM model testing task was collected by a weather station operated by Earth Networks (Germantown MD, USA) [62], situated at latitude 35.3069\({}^{\circ}\) N and longitude 25.0819\({}^{\circ}\) E, at an elevation of 324 m above sea level. This station serves as a robust source of high-resolution meteorological data. The data set covers precisely one year, from 6 November 2018 at 18:38 local time to 8 November 2019 at 15:19 local time, with measurements taken every 10 min, providing a sequential and continuous time series ideal for research purposes. This time window was selected as it was the only period during which continuous time series data could be retrieved without missing values. The station records parameters such as the average temperature over 10 min (\({}^{\circ}\)C), the lowest temperature during that interval, the highest temperature within the same timeframe (both in \({}^{\circ}\)C), dew point temperature (\({}^{\circ}\)C), wet bulb temperature (\({}^{\circ}\)C), relative humidity (%), atmospheric pressure at mean sea level (hPa), accumulated daily rainfall resetting at the end of each day (mm), wind speed (m/s), and wind direction (degrees). The wind direction was analyzed in sine and cosine values to prevent discontinuities at 0\({}^{\circ}\)/360\({}^{\circ}\) and to maintain angular relationships for smoother mathematical operations in our machine learning models. A typical wind field for the island of Crete, where the weather station is located, is depicted in Figure 2 for a date included in the data set. The flow field was generated using WRF numerical weather prediction model simulations [63].  
The employed data set consists of variables that exhibit diverse temporal behavior--some, like temperature, display strong repeating patterns, while others, such as wind speed, exhibit chaotic dynamics. This diversity makes the data set a suitable testbed for evaluating the performance of time series modelling approaches, particularly Long Short-Term Memory (LSTM) networks, which are well-suited for capturing both long-term dependencies and short-term variations in sequential data. While the data set is specific  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Units** & **Activation** & **Optimizer** & **Batch Size** & **Epochs** & **Data Scaling** & **Data Split (Tr-Te)** & **Time Steps** \\ \hline
10 & ReLU & Adam & 16 & 10 & MinMax Scaler & 90-10 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: LSTM-H model optimal settings for forecasting meteorological variables. (Tr-Te) denotes Training–Testing data split.  
to meteorological applications, the methodological framework employed in this study is broadly applicable to other time series domains. The ability of LSTMs to learn complex temporal patterns makes them effective for analyzing data sets in finance, biomedical sciences, and other fields where time-dependent relationships are critical. However, while our findings highlight the robustness of LSTM-based modelling for capturing different types of temporal dependencies, domain-specific adjustments may be necessary to optimize performance in contexts beyond weather forecasting.