Digital TV broadcasting networks rely on alarm systems to monitor equipment and service health. However, the large volume of alarms, many of which are non-critical, makes it difficult for Network Operation Center (NOC) operators to identify early signs of service-affecting incidents. This paper presents an automated incident prediction system based on machine learning techniques, developed with a Spanish digital TV operator, to address this challenge. The system is tested under three synthetic scenarios—Baseline, Degraded, and Alarm-storm—that simulate increasing levels of network degradation. Alarm data are encoded under four contextual settings: (i) without additional information; (ii) including pattern-based features to capture interactions with other network elements; (iii) incorporating time-based statistical features extracted from alarm activity; and (iv) combining both types of contextual information. Results show that incorporating temporal context significantly improves detection, especially in highly imbalanced conditions where incidents are rare. LightGBM stands out among the evaluated classifiers, achieving the highest balanced accuracy when time-based statistical features are used.