AutoML addresses the challenge of multiple values for a single timestamp in forecasting by averaging these values. This method ensures a single, representative value per timestamp, facilitating smoother model training and forecasting. Averaging helps mitigate noise from multiple measurements, offering a stable time series representation. Alternatives like selecting the maximum or minimum value could introduce bias, while discarding extra values leads to data loss. Depending on the scenario, other strategies such as weighted averages or median values might be considered for their potential to better handle outliers or specific data characteristics.