SAX

SAX (Symbolic Aggregate approXimation) is a technique used to simplify complex time series data, making it easier to analyze. It transforms continuous data into a sequence of symbols (like letters), by dividing the data into equal segments, calculating their average, and then assigning each average to a symbol based on its value range. This reduces the data's complexity while preserving its key patterns, enabling faster comparison and pattern recognition across large datasets, such as in financial analysis or sensor monitoring, without losing essential information about the trends and behaviors within the data.