PAA-based Piecewise Linear Representation Algorithm

The PAA-based piecewise linear representation algorithm is a method for segmenting time series into equal-width windows. In this algorithm, the time series within each window is represented by the mean value of that window, producing a piecewise linear representation of the original time series. The algorithm takes the number of segments K as its input parameter, allowing users to obtain different representation results by adjusting the value of K. Implementation-wise, this typically involves dividing the time series into K equal segments using array slicing operations and computing the mean values using statistical functions. Furthermore, the algorithm can process various types of time series data, such as stock prices and meteorological data. Consequently, the PAA-based piecewise linear representation algorithm shows broad application prospects in time series analysis and forecasting, particularly in data compression and pattern recognition tasks where it reduces dimensionality while preserving key trend information.