作　　者： ;

机构地区： 华南理工大学

出　　处： 《沈阳工业大学学报》 2014年第1期79-85,共7页

摘　　要： 为了降低基于固定窗口进行分段线性表示的拟合误差，通过分析抽象出时间序列数据中存在的6种不同数据变化模式，以此设计自适应性窗口装载不同模式数据并求解分段点，从而形成了基于自适应窗口的分段线性表示算法（AW-PLR算法）．基于真实GPS浮动车数据及通用实验数据的结果表明，AW-PLR算法能够通过调整波动阈值r控制压缩率和拟合误差，且在相同压缩率的情况下，AW—PLR算法比SEEP算法平均降低约24％-27％的拟合误差． In order to reduce the fitting error of fixed window based piecewise linear representation algorithm, six kinds of data change patterns in the time series data were abstracted through the analysis. Therefore, the adaptive window was designed to load the different patterns of data and solve the segmentation point, and an adaptive window based piecewise linear representation （AW-PLR） algorithm was established. The results based on the real GPS floating car data and general test data show that the compression ratio and fitting error can be controlled through adjusting the threshold r with the AW-PLR algorithm. Under the same compression ratio, the AW-PLR algorithm can averagely reduce the fitting error by 24% -27%, compared with the SEEP algorithm.

关 键 词： 自适应性 窗口宽度 分段线性表示 数据变化模式 时间序列数据 数据压缩 数据拟合

分 类 号： [TP274]

领　　域： []