## 4 Experiments
### Dataset  
The data used in this study come from the Madrid City Council (Spain), are open access, and are available on its website [24]. The dataset used in this study contains the following information:  
1. **NMT (Measurement Station Number)**: A unique numerical identifier for each station where noise measurements are taken. This code allows for the specific identification of each station's location. 2. **Year**: The year in which the measurements were taken, represented as a number. 3. **Month:** The month when the noise data were recorded, indicated by a number from 1 to 12. 4. **Day:** The day of the month when the measurement was taken, represented by a numerical value between 1 and 31. 5. **Type:** Time of day corresponding to the measurement. Expressed using the following options: * \"D\": Daytime. * \"N\": Nighttime. 6. **LAeq (Equivalent Continuous Sound Level):** The average sound pressure level during the measurement period, expressed in decibels (dB). This value reflects overall exposure to noise over the observed period. 7. **L1:** Sound pressure level exceeded during 1% of the observation time. Used to identify noise peaks that occur rarely. 8. **L10:** Sound pressure level exceeded during 10% of the observation time. Typically associated with frequent loud noises. 9. **L50:** Sound pressure level exceeded during 50% of the observation time. Often used as a reference for \"typical\" or \"median\" noise levels in an area. 10. **L90:** Sound pressure level exceeded during 90% of the observation time. Usually represents background or constant noise levels. 11. **L99:** Sound pressure level exceeded during 99% of the observation time. Indicates the most persistent noise levels, reflecting minimum values present most of the time. The target variable in this study is **LAeq** (Equivalent Continuous A-weighted Sound Pressure Level), which represents the continuous equivalent A-weighted sound pressure level in decibels (dB) over a specified time period. This variable is calculated to reflect the total energy of noise exposure, providing a representative measure of the average noise level in a given environment. Using **LAeq** as the target variable is appropriate because it captures overall environmental noise exposure, integrating both fluctuating noise levels and their cumulative effects over time. Since **LAeq** is widely recognized in noise studies and public health regulations, it provides a solid framework for assessing and predicting noise pollution across various urban contexts. According to WHO guidelines, the acceptable noise levels for urban areas are as follows [25; 26]:  
* Environmental noise during the day (outdoor): 55 dB(A)--maximum recommended to avoid annoyance. * Environmental noise at night (outdoor): 40 dB(A)--recommended limit to avoid adverse health effects related to sleep. * Indoor noise during sleep: 30 dB(A)--ideal limit for indoor environments such as bedrooms at night. * Maximum noise in short events (peaks) during the night: 45 dB(A)--limit for isolated noise events like passing cars or sirens. The dataset consists of approximately 240,932 measurements collected by 31 stations over a period of 11 years. Measurements are taken during two periods--day and night--due to the different recommended thresholds for health protection. Since the goal is to predict 14 future time intervals, our model focuses on estimating noise levels for the next 7 days. The average levels recorded by the stations are presented in Figure 3.  
As shown, the COVID-19 pandemic period exhibits anomalous measurement points, which aligns with expectations given the reduced urban activity and traffic during that time. To mitigate the impact of these anomalies on model performance, data from 2020 and 2021 were excluded from the training process. Additional features were incorporated to enhance the models. For instance, weekends may exhibit distinct patterns compared to weekdays, and holidays may influence measurements at specific noise monitoring stations, leading to unique patterns.