经典的时间序列符号化算法SAX，该算法通过将一条时间序列等区间划分，利用每个区间的均值代表该区间序列，进而采用相应的符号进行描述，该算法可以实现时间序列的符号化表示，达到降维的目的，并能够通过MATLAB进行可视化描述

SAX符号化序列范例源码 -------------------- timeseries2symbol.m: -------------------- This function takes in a time series and convert it to string(s). There are two options: 1. Convert the entire time series to ONE string 2. Use sliding windows, extract the subsequences and convert these subsequences to strings For the first option, simply enter the length of the time series as "N" ex. We have a time series of length 32 and we want to convert it to a 8-symbol string, with alphabet size 3: timeseries2symbol(data, 32, 8, 3) For the second option, enter the desired sliding window length as "N" ex. We have a time series of length 32 and we want to extract subsequences of length 16 using sliding windows, and convert the subsequences to 8-symbol strings, with alphabet size 3: timeseries2symbol(data, 16, 8, 3) Input: data is the raw time series. N is the length of sliding window (use the length of the raw time series instead if you don't want to have sliding windows) n is the number of symbols in the low dimensional approximation of the sub sequence. alphabet_size is the number of discrete symbols. 2 <= alphabet_size <= 10, although alphabet_size = 2 is a special "useless" case. Output: symbolic_data: matrix of symbolic data (no-repetition). If consecutive subsequences have the same string, then only the first occurrence is recorded, with a pointer to its location stored in "pointers" pointers: location of the first occurrences of the strings N/n must be an integer, otherwise the program will give a warning, and abort. The variable "win_size" is assigned to N/n, this is the number of data points on the raw time series that will be mapped to a single symbol, and can be imagined as the "compression rate". The symbolic data is returned in "symbolic_data", with pointers to th

在ArcGIS中我们可以根据某一字段对点线面要素进行符号化展示，下面附上一个有关AE符号化的例子，希望对大家有用