Temporal Association Rules extend traditional association rule mining by incorporating the dimension of time, allowing for the discovery of patterns and relationships that change over time in temporal datasets. This approach is crucial for applications where timing and sequence of events are as important as the events themselves, such as in market basket analysis, stock price prediction, and healthcare monitoring.