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最近咱们被客户要求撰写对于 KShape 的钻研报告，包含一些图形和统计输入。
时序数据的聚类办法，该算法依照以下流程执行。
- 应用基于相互关测量的间隔标度（基于形态的间隔：SBD）
- 依据 1 计算工夫序列聚类的质心。（一种新的基于质心的聚类算法，可保留工夫序列的形态）
- 划分成每个簇的办法和个别的 kmeans 一样，然而在计算间隔尺度和重心的时候应用下面的 1 和 2。
import pandas as pd # 读取数据帧，将其转化为工夫序列数组，并将其存储在一个列表中 tata = [] for i, df in enmee(dfs):
# 查看每个工夫序列数据的最大长度。for ts in tsda:
if len(s) > ln_a:
lenmx = len(ts)
# 给出最初一个数据，以调整工夫序列数据的长度 for i, ts in enumerate(tsdata):
dta[i] = ts + [ts[-1]] * n_dd
# 转换为矢量 stack_list = [] for j in range(len(timeseries_dataset)):
stack_list.append(data)
# 转换为一维数组 trasfome_daa = np.stack(ack_ist, axis=0)
return trafoed_data数据集筹备
# 文件列表 flnes= soted(go.ob('mpldat/smeda*.csv'))# 从文件中加载数据帧并将其存储在一个列表中。for ienme in fiemes:
df = pd.read_csv(filnme, indx_cl=one,hadr=0) flt.append(df)聚类后果的可视化
# 为了计算穿插关系，须要对它们进行归一化解决。# TimeSeriesScalerMeanVariance 将是对数据进行规范化的类。sac_da = TimeeiesalerMVarne(mu=0.0, std=1.0).fit_trnform(tranfome_data)# KShape 类的实例化。ks = KShpe(_clusrs=2, n_nit=10, vrboe=True, rano_stte=sed)
yprd = ks.ft_reitsak_ata)# 聚类和可视化 plt.tight_layout()
plt.show()点击题目查阅往期内容
R 语言 k -Shape 工夫序列聚类办法对股票价格工夫序列聚类
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用肘法计算簇数
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什么是肘法... - 计算从每个点到簇核心的间隔的平方和，指定为簇内误差平方和 (SSE)。
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它是一种更改簇数，绘制每个 SSE 值，并将像“肘”一样蜿蜒的点设置为最佳簇数的办法。
# 计算到 1~10 个群组 for i in range(1,11):
#进行聚类计算。
ks.fit(sacdta)
#KS.fit 给出 KS.inrta_ disorons.append(ks.netia_)
plt.plot(range(1,11), disorins, marker=’o’)
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获取全文残缺材料。本文选自《**Python 用 KShape 对工夫序列进行聚类和肘办法确定最优聚类数 k 可视化 **》。** 点击题目查阅往期内容 **
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