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如何以编程方式检测数据序列的片段以适合不同的曲线？
是否有记录在案的算法将给定数据集的各个部分分成最适合的不同曲线？ 例如，大多数查看此数据图表的人都可以将其轻松分为3部分：正弦曲线段，线性段和反指数段。实际上，我是用正弦波，一条直线和一个简单的指数公式制作的。 是否存在用于查找类似零件的现有算法，然后可以将这些零件分别拟合到各种曲线/线上以形成一种最佳数据子集的复合系列？ 请注意，尽管示例中段的末端几乎对齐，但这并不一定是这种情况。在分段截止时，这些值也可能突然震荡。也许这些情况将更容易发现。 更新：这是一小部分真实数据的图像： 更新2：这是一组非常小的实际数据集（仅509个数据点）： 4,53,53,53,53,58,56,52,49,52,56,51,44,39,39,39,37,33,27,21,18,12,19,30,45,66,92,118,135,148,153,160,168,174,181,187,191,190,191,192,194,194,194,193,193,201,200,199,199,199,197,193,190,187,176,162,157,154,144,126,110,87,74,57,46,44,51,60,65,66,90,106,99,87,84,85,83,91,95,99,101,102,102,103,105,110,107,108,135,171,171,141,120,78,42,44,52,54,103,128,82,103,46,27,73,123,125,77,24,30,27,36,42,49,32,55,20,16,21,31,78,140,116,99,58,139,70,22,44,7,48,32,18,16,25,16,17,35,29,11,13,8,8,18,14,0,10,18,2,1,4,0,61,87,91,2,0,2,9,40,21,2,14,5,9,49,116,100,114,115,62,41,119,191,190,164,156,109,37,15,0,5,1,0,0,2,4,2,0,48,129,168,112,98,95,119,125,191,241,209,229,230,231,246,249,240,99,32,0,0,2,13,28,39,15,15,19,31,47,61,92,91,99,108,114,118,121,125,129,129,125,125,131,135,138,142,147,141,149,153,152,153,159,161,158,158,162,167,171,173,174,176,178,184,190,190,185,190,200,199,189,196,197,197,196,199,200,195,187,191,192,190,186,184,184,179,173,171,170,164,156,155,156,151,141,141,139,143,143,140,146,145,130,126,127,127,125,122,122,127,131,134,140,150,160,166,175,192,208,243,251,255,255,255,249,221,190,181,181,181,181,179,173,165,159,153,162,169,165,154,144,142,145,136,134,131,130,128,124,119,115,103,78,54,40,25,8,2,7,12,25,13,22,15,33,34,57,71,48,16,1,2,0,2,21,112,174,191,190,152,153,161,159,153,71,16,28,3,4,0,14,26,30,26,15,12,19,21,18,53,89,125,139,140,142,141,135,136,140,159,170,173,176,184,180,170,167,168,170,167,161,163,170,164,161,160,163,163,160,160,163,169,166,161,156,155,156,158,160,150,149,149,151,154,156,156,156,151,149,150,153,154,151,146,144,149,150,151,152,151,150,148,147,144,141,137,133,130,128,128,128,136,143,159,180,196,205,212,218,222,225,227,227,225,223,222,222,221,220,220,220,220,221,222,223,221,223,225,226,227,228,232,235,234,236,238,240,241,240,239,237,238,240,240,237,236,239,238,235 这，绘制，与appoximate的位置有些已知的真实世界元素边用虚线标记，奢侈品，我们通常不会有： 然而，我们确实拥有的一种奢望是事后观察：就我而言，数据不是时间序列，而是与空间相关的。一次分析整个数据集（通常为5000-15000个数据点）是有意义的，而不是持续进行。