Temporal decay refers to the diminishing influence or relevance of data, signals, or information over time, often necessitating adjustments in models or systems to maintain accuracy and relevance. This concept is crucial in fields like machine learning, where older data may not accurately represent current trends or conditions, requiring mechanisms to prioritize recent information.