Удалить шаги во временных рядах - PullRequest
Новая
       34

Удалить шаги во временных рядах

1 голос
/ 11 февраля 2020

Я анализирую повышение температуры ящериц. Проблема в том, что животные иногда двигаются, перемещая термометр, который мы используем для измерений. Это приводит к пикам (несколько измерений очень высоко или низко) и шагам (внезапные смещения кривой). Я хотел очистить мои кривые от этого шума, удалив шипы и совместив две части кривой, разделенные каждым шагом. Я думаю, что шипы можно легко сгладить, но как насчет шагов? Примеры временных рядов с шагами, отмеченными желтым, и всплесками красным

Это пример из моего кадра данных 

structure(list(ID = structure(c(183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 
183L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 194L, 
194L, 194L, 194L), .Label = c("101", "102", "104", "106", "107", 
"109", "110", "111", "112", "113", "114", "115", "116", "118", 
"119", "121", "122", "123", "124", "125", "126", "128", "129", 
"130", "132", "133", "134", "157", "158", "161", "163", "164", 
"165", "166", "167", "168", "169", "170", "171", "172", "173", 
"174", "175", "177", "178", "179", "180", "181", "182", "183", 
"186", "187", "188", "189", "191", "192", "193", "194", "195", 
"196", "198", "199", "201", "202", "203", "204", "205", "207", 
"208", "209", "210", "211", "213", "214", "215", "218", "219", 
"220", "221", "222", "223", "224", "225", "226", "227", "228", 
"229", "230", "233", "235", "307", "308", "310", "311", "312", 
"313", "314", "315", "316", "317", "318", "319", "320", "321", 
"322", "323", "324", "325", "326", "327", "328", "329", "330", 
"331", "332", "333", "334", "335", "336", "339", "340", "341", 
"343", "344", "346", "347", "348", "349", "350", "351", "352", 
"353", "354", "355", "356", "357", "358", "58", "59", "60", "61", 
"62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", 
"73", "74", "75", "76", "78", "79", "80", "81", "82", "83", "84", 
"85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", 
"96", "97", "98", "99", "F04000017", "F04001300", "F04060000", 
"F04070000", "F04080000", "F05000017", "F05000020", "F05060000", 
"F05070000", "FSUM", "M03100000", "M04000016", "M04090000", "M04100000", 
"M05000016", "M05000018", "M05080000", "M05090000", "M05100000", 
"MSUM"), class = "factor"), TIME = c(1600, 0, 180, 280, 1270, 
190, 1570, 100, 630, 110, 1530, 790, 90, 650, 910, 1520, 460, 
270, 710, 1240, 1610, 550, 470, 1320, 360, 1220, 860, 540, 290, 
1330, 1180, 170, 1700, 990, 1060, 420, 10, 330, 1110, 1160, 890, 
260, 620, 1140, 1420, 380, 300, 1650, 1430, 200, 490, 1590, 150, 
430, 720, 950, 730, 1660, 1260, 740, 810, 780, 1370, 960, 1130, 
1210, 1670, 1010, 760, 1280, 1450, 770, 1070, 1000, 1030, 690, 
80, 1040, 1050, 160, 1350, 1230, 1630, 640, 1390, 1460, 1100, 
850, 1120, 390, 1170, 980, 320, 590, 520, 1090, 560, 870, 1020, 
750, 140, 700, 70, 1500, 340, 1290, 530, 840, 230, 370, 1440, 
1200, 1480, 30, 1400, 210, 1300, 480, 450, 1580, 1560, 410, 1360, 
900, 670, 1540, 50, 500, 400, 1380, 930, 580, 1680, 600, 1340, 
240, 1190, 570, 1640, 940, 350, 1550, 830, 310, 880, 1080, 920, 
1490, 610, 220, 1470, 800, 820, 40, 1250, 130, 60, 680, 20, 970, 
1620, 440, 1690, 120, 510, 1410, 660, 250, 1310, 1150, 1510, 
1230, 1540, 320, 1380, 680, 410, 480, 580, 130, 670, 1200, 1900, 
1410, 1470, 1950, 400, 870, 970, 1740, 500, 1300, 1220, 240, 
660, 1100, 20, 490, 590, 900, 820, 1910, 920, 690, 1290, 1710, 
750, 1860, 1840, 880, 1250, 2070, 640, 1580, 1310, 800, 1000, 
2090, 1760, 330, 760, 40, 600, 1800, 200, 1730, 2080, 470, 30, 
1490, 2030, 860, 1620, 250, 1880, 790, 90, 360, 1520, 460, 180, 
1770, 1450, 1940, 1370, 1180, 290, 2060, 1240, 1500, 1480, 510, 
980, 1610, 1630, 950, 1980, 990, 140, 60, 110, 1680, 10, 550, 
1700, 1750, 1270, 1690, 230, 2010, 1070, 910, 300, 1850, 1140, 
1350, 930, 170, 1650, 1170, 1820, 1040, 1420, 120, 1150, 50, 
960, 1560, 1790, 630, 280, 740, 1890, 2020, 370, 2040, 2050, 
310, 380, 1920, 340, 1030, 430, 1330, 1670, 1960, 830, 1590, 
420, 1400, 560, 1360, 1460, 1440, 1120, 1870, 1720, 1130, 730, 
390, 850, 520, 1090, 1550, 100, 1930, 1010, 440, 210, 1390, 720, 
1190, 450, 770, 1320, 220, 1640, 570, 2100, 1210, 650, 2000, 
840, 1340, 1830, 530, 1110, 1260, 890, 700, 80, 810, 1060, 260, 
1510, 1600, 70, 1430, 1280, 1530, 1020, 540, 940, 1050, 710, 
1660, 1570, 270, 610, 620, 780, 1780, 350, 1970, 0, 1160, 1990, 
190, 150, 1080, 160, 1810), LIZ = c(33.88628, 16.01848, 19.07537, 
20.7521, 31.20864, 19.28933, 33.81645, 17.74926, 25.17476, 17.96775, 
35.05947, 27.00651, 17.59508, 25.4069, 28.208, 34.99299, 22.88728, 
20.59275, 26.18355, 31.03587, 33.947, 24.10997, 23.02703, 33.7095, 
22.17608, 30.91595, 27.75973, 23.97134, 20.93754, 33.80886, 30.68742, 
18.91375, 33.92175, 29.09865, 29.63962, 22.70535, 16.21696, 21.63548, 
30.08059, 30.53127, 27.98175, 20.43409, 25.05877, 30.36036, 34.46184, 
22.44901, 21.10243, 33.94136, 34.52333, 19.46398, 23.28305, 33.80731, 
18.61317, 22.47734, 26.25653, 28.72091, 26.38854, 33.93099, 31.15667, 
26.51461, 27.30298, 26.90585, 34.10386, 28.83213, 30.1994, 30.83049, 
33.89071, 29.26265, 26.67329, 32.40561, 34.64661, 26.80018, 29.67405, 
29.16813, 29.42029, 25.87777, 17.42933, 29.50874, 29.60253, 18.75925, 
33.97794, 30.99272, 33.94607, 25.2893, 34.24567, 34.68945, 30.0688, 
27.65221, 30.13411, 22.59461, 30.63778, 28.96221, 21.41397, 24.58403, 
23.68083, 29.85481, 24.22983, 27.8342, 29.36583, 26.60143, 18.46704, 
26.03891, 17.26645, 34.9014, 21.84469, 33.39574, 23.82, 27.57049, 
19.93821, 22.30472, 34.60674, 30.77051, 34.79948, 16.55761, 34.33158, 
19.62092, 33.46234, 23.15645, 22.68871, 33.79304, 33.88377, 22.99619, 
34.04042, 28.1129, 25.6251, 34.21122, 16.89074, 23.43516, 22.78885, 
34.17764, 28.47507, 24.45472, 33.8742, 24.72735, 33.90453, 20.10577, 
30.73417, 24.35771, 33.93048, 28.62741, 21.99781, 33.97049, 27.48719, 
21.25535, 27.92438, 29.80396, 28.35731, 34.8615, 24.93956, 19.783, 
34.76926, 27.19699, 27.40897, 16.72252, 31.08047, 18.32881, 17.0416, 
25.72955, 16.38935, 28.8926, 33.97636, 22.53829, 33.86432, 18.22302, 
23.55972, 34.38583, 25.50873, 20.27311, 33.6045, 30.43855, 34.93784, 
35.288345, 36.257075, 25.391195, 36.09727, 30.813695, 27.044815, 
28.04567, 29.548165, 20.606055, 30.69551, 35.129335, 37.32639, 
36.37374, 36.308585, 37.41784, 26.86707, 32.548155, 33.36402, 
37.37834, 28.385865, 35.88449, 35.231535, 23.53661, 30.580155, 
34.58997, 16.9712, 28.21824, 29.69837, 32.803, 32.16565, 37.3345, 
32.96087, 30.917135, 35.835545, 37.40881, 31.57274, 37.28267, 
37.3545, 32.635805, 35.639945, 37.55586, 30.322245, 36.361875, 
35.93363, 32.083515, 33.9122, 37.57389, 37.38447, 25.60093, 31.70867, 
17.74926, 29.83628, 37.32308, 22.486975, 37.37677, 37.54575, 
27.8517, 17.37036, 36.13542, 37.58466, 32.4728, 36.84912, 23.79395, 
37.2999, 32.03574, 19.402135, 27.835535, 36.185425, 27.68629, 
21.91643, 37.37574, 36.612895, 37.38406, 36.11211, 35.03594, 
24.74032, 37.55308, 35.4775, 36.152685, 36.125095, 28.51215, 
33.614975, 36.71473, 36.859965, 33.190425, 37.57786, 33.82105, 
20.911715, 18.444165, 19.9942, 36.89602, 16.551, 29.161815, 37.4295, 
37.38779, 35.75298, 36.91141, 23.29514, 37.59916, 34.393665, 
32.88079, 24.885135, 37.30579, 34.812115, 36.05718, 33.03272, 
21.730805, 36.877595, 34.980885, 37.34123, 34.19463, 36.436875, 
20.27451, 34.880975, 18.10635, 33.268755, 36.24434, 37.33012, 
30.18474, 24.530635, 31.445215, 37.32058, 37.6003, 36.417575, 
37.57818, 37.56227, 25.15269, 26.548085, 37.34238, 25.799625, 
34.12894, 27.14156, 36.0024, 36.89349, 37.48383, 32.23698, 36.386895, 
27.013425, 36.295295, 29.29934, 36.091595, 36.642735, 36.5601, 
34.706135, 37.27491, 37.38146, 34.758915, 31.35675, 26.709435, 
32.374665, 28.69313, 34.52595, 36.22561, 19.717425, 37.35049, 
33.991875, 27.288855, 22.76699, 36.179625, 31.25419, 35.08752, 
27.507965, 31.821405, 35.97357, 23.037415, 36.86924, 29.408195, 
37.60048, 35.18184, 30.45653, 37.58567, 32.27861, 36.04241, 37.34733, 
28.86293, 34.64831, 35.684655, 32.71743, 31.01652, 19.07221, 
32.1161, 34.32663, 24.059185, 36.16283, 36.413085, 18.77961, 
36.49902, 35.7932, 36.214135, 34.05778, 29.01832, 33.111965, 
34.2606, 31.14351, 36.88914, 36.326515, 24.30303, 29.97399, 30.103995, 
31.92968, 37.36615, 26.01068, 37.52906, 16.12443, 34.929045, 
37.59149, 22.175545, 21.19651, 34.4613, 21.473975, 37.33004), 
    COP = c(22.39478, 22.68049, 22.27224, 22.19685, 22.16573, 
    22.26254, 22.34891, 22.41931, 22.15782, 22.39973, 22.31492, 
    22.13082, 22.44822, 22.16036, 22.13963, 22.3111, 22.15529, 
    22.20868, 22.14849, 22.16546, 22.40751, 22.15873, 22.15807, 
    22.15667, 22.17006, 22.16576, 22.13619, 22.15633, 22.18792, 
    22.15968, 22.15784, 22.28282, 22.39207, 22.15544, 22.15307, 
    22.15378, 22.65061, 22.18484, 22.14693, 22.1517, 22.14945, 
    22.22223, 22.15782, 22.15069, 22.18498, 22.17095, 22.18882, 
    22.44182, 22.18867, 22.25752, 22.16363, 22.37156, 22.33576, 
    22.14907, 22.15658, 22.15133, 22.1547, 22.43926, 22.16738, 
    22.15422, 22.13264, 22.14114, 22.17317, 22.15817, 22.14417, 
    22.16643, 22.43323, 22.14401, 22.14921, 22.15957, 22.22265, 
    22.14089, 22.14463, 22.14593, 22.14691, 22.14661, 22.47322, 
    22.15314, 22.15694, 22.3083, 22.16482, 22.16823, 22.42285, 
    22.16284, 22.17225, 22.2391, 22.1465, 22.12234, 22.14539, 
    22.15492, 22.14823, 22.15392, 22.18663, 22.14773, 22.16047, 
    22.14056, 22.15791, 22.14152, 22.14251, 22.1509, 22.35001, 
    22.14744, 22.49713, 22.28185, 22.18089, 22.15846, 22.15929, 
    22.12327, 22.23335, 22.16951, 22.20456, 22.16193, 22.25876, 
    22.60373, 22.17513, 22.25065, 22.15608, 22.16255, 22.14552, 
    22.36192, 22.3395, 22.15159, 22.16513, 22.14421, 22.15804, 
    22.31474, 22.5485, 22.16658, 22.15816, 22.16993, 22.14259, 
    22.14321, 22.4235, 22.16097, 22.16565, 22.22893, 22.16082, 
    22.14911, 22.43463, 22.14959, 22.16988, 22.32926, 22.12439, 
    22.18906, 22.14787, 22.143, 22.14448, 22.26607, 22.16002, 
    22.24653, 22.25201, 22.13589, 22.13117, 22.578, 22.16947, 
    22.36273, 22.51722, 22.15239, 22.62479, 22.15932, 22.41646, 
    22.14778, 22.41367, 22.38414, 22.16356, 22.18521, 22.16367, 
    22.22729, 22.16007, 22.15065, 22.29892, 21.72296, 21.62249, 
    22.017515, 21.693785, 21.70153, 21.87866, 21.81236, 21.732425, 
    22.36322, 21.70375, 21.72846, 21.91553, 21.68792, 21.660505, 
    22.00908, 21.89052, 21.763825, 21.760305, 21.83179, 21.79684, 
    21.71059, 21.726415, 22.149185, 21.70785, 21.75106, 22.571055, 
    21.804205, 21.7291, 21.761135, 21.747215, 21.92869, 21.76272, 
    21.705115, 21.712995, 21.83027, 21.708885, 21.83844, 21.84054, 
    21.768415, 21.71803, 22.11175, 21.70465, 21.60251, 21.70955, 
    21.742635, 21.753505, 22.13507, 21.82251, 21.999255, 21.717505, 
    22.53302, 21.72247, 21.82229, 22.22044, 21.829, 22.12267, 
    21.82473, 22.55087, 21.6558, 22.09163, 21.76516, 21.593285, 
    22.13412, 21.86871, 21.73596, 22.43231, 21.943965, 21.636055, 
    21.836155, 22.26584, 21.82428, 21.672995, 21.98196, 21.694165, 
    21.73181, 22.066775, 22.10273, 21.71976, 21.64993, 21.65895, 
    21.788935, 21.759365, 21.59079, 21.59272, 21.762905, 22.04811, 
    21.755645, 22.34494, 22.4932, 22.397445, 21.59399, 22.582895, 
    21.750065, 21.84207, 21.82297, 21.712635, 21.593685, 22.164485, 
    22.07698, 21.7517, 21.760385, 22.04753, 21.8382, 21.741645, 
    21.69029, 21.759415, 22.28582, 21.590015, 21.73355, 21.83193, 
    21.75102, 21.68036, 22.378585, 21.73784, 22.51333, 21.75792, 
    21.609515, 21.82392, 21.70592, 22.084645, 21.708, 21.89372, 
    22.08329, 21.93241, 22.10217, 22.10085, 22.030495, 21.91551, 
    21.94469, 21.979055, 21.75095, 21.858405, 21.698245, 21.59609, 
    22.02914, 21.755635, 21.593795, 21.86841, 21.689295, 21.739825, 
    21.691495, 21.66596, 21.674615, 21.74691, 21.84458, 21.82984, 
    21.741525, 21.710355, 21.90157, 21.762305, 21.77891, 21.750345, 
    21.61608, 22.41471, 21.95989, 21.7551, 21.84604, 22.198325, 
    21.691115, 21.709805, 21.732425, 21.84097, 21.723055, 21.70937, 
    22.179165, 21.58997, 21.732705, 22.13691, 21.725925, 21.70574, 
    22.06154, 21.755495, 21.692555, 21.83686, 21.77328, 21.74777, 
    21.716135, 21.761175, 21.709325, 22.451175, 21.749225, 21.750545, 
    22.117925, 21.636325, 21.59097, 22.47463, 21.67836, 21.715015, 
    21.63479, 21.75122, 21.762415, 21.764115, 21.751305, 21.70731, 
    21.591635, 21.606505, 22.09898, 21.71826, 21.71403, 21.727555, 
    21.81842, 21.963065, 22.03673, 22.597065, 21.73344, 22.05635, 
    22.243885, 22.325185, 21.75266, 22.3091, 21.82619), BLK = c(28.64989, 
    15.80412, 20.22131, 22.01231, 28.0591, 20.41488, 28.57393, 
    18.42224, 25.81396, 18.66597, 28.51571, 26.66224, 18.17439, 
    25.95201, 27.20529, 28.51703, 24.38048, 21.85311, 26.30937, 
    28.02257, 28.6773, 25.22704, 24.48749, 28.1236, 23.18583, 
    27.98485, 27.00689, 25.14024, 22.16927, 28.1335, 27.89662, 
    20.00687, 28.67155, 27.44175, 27.62627, 23.92489, 16.09932, 
    22.78544, 27.75227, 27.83675, 27.13084, 21.69284, 25.74085, 
    27.80579, 28.28594, 23.44893, 22.33534, 28.7166, 28.29154, 
    20.61311, 24.68904, 28.60215, 19.59335, 24.03528, 26.36296, 
    27.34839, 26.40381, 28.7205, 28.04637, 26.45016, 26.76595, 
    26.62852, 28.18898, 27.38003, 27.79384, 27.96629, 28.71652, 
    27.47686, 26.54292, 28.06953, 28.37088, 26.5854, 27.64778, 
    27.45691, 27.54817, 26.17697, 17.91728, 27.57453, 27.59667, 
    19.79719, 28.15391, 28.01052, 28.70145, 25.8918, 28.19863, 
    28.40318, 27.73245, 26.95575, 27.77656, 23.56018, 27.86381, 
    27.42115, 22.64232, 25.52273, 24.96468, 27.69186, 25.29434, 
    27.05567, 27.51177, 26.4991, 19.37665, 26.24547, 17.66023, 
    28.45211, 22.92771, 28.08868, 25.05819, 26.9147, 21.16986, 
    23.31623, 28.31888, 27.94109, 28.41742, 16.63277, 28.21869, 
    20.81001, 28.10906, 24.58388, 24.26295, 28.59208, 28.56833, 
    23.80437, 28.16685, 27.16407, 26.06582, 28.53314, 17.15336, 
    24.78023, 23.69012, 28.19161, 27.27431, 25.4426, 28.70709, 
    25.60821, 28.15239, 21.35028, 27.92147, 25.36057, 28.71294, 
    27.31714, 23.05455, 28.55633, 26.8609, 22.49237, 27.09901, 
    27.67287, 27.23569, 28.43207, 25.6761, 20.99499, 28.41302, 
    26.72662, 26.81219, 16.89905, 28.04126, 19.14027, 17.39888, 
    26.11992, 16.37336, 27.39998, 28.69902, 24.15099, 28.69344, 
    18.90929, 24.86793, 28.25387, 26.01419, 21.52158, 28.12068, 
    27.81882, 28.48342, 27.96163, 28.197645, 22.70333, 28.117945, 
    26.073955, 23.85303, 24.593665, 25.413525, 19.11083, 26.00622, 
    27.90615, 27.98023, 28.14475, 28.161525, 28.08333, 23.7368, 
    27.01432, 27.341995, 27.83233, 24.77127, 28.08601, 27.96112, 
    21.39113, 25.949465, 27.68429, 15.858655, 24.68182, 25.48676, 
    27.12372, 26.823595, 27.98649, 27.17823, 26.13994, 28.074105, 
    27.80747, 26.44518, 27.88772, 27.88865, 27.054655, 27.983055, 
    28.14709, 25.80761, 28.22729, 28.087765, 26.741425, 27.415515, 
    28.16615, 27.82628, 22.852945, 26.515965, 16.55507, 25.558165, 
    27.86497, 20.59904, 27.82338, 28.15537, 24.503635, 16.208215, 
    28.18434, 28.12301, 26.98152, 28.238175, 21.57631, 27.93972, 
    26.69019, 18.11697, 23.249475, 28.194915, 24.400325, 20.18436, 
    27.83363, 28.152575, 28.05914, 28.1046, 27.87944, 22.246695, 
    28.13895, 27.97113, 28.17759, 28.17134, 24.852555, 27.36403, 
    28.231645, 28.240475, 27.28467, 28.09842, 27.38207, 19.33762, 
    17.208895, 18.63795, 28.32022, 15.478795, 25.178505, 27.81531, 
    27.83335, 28.025865, 28.32901, 21.198015, 28.12642, 27.594495, 
    27.149095, 22.40141, 27.87721, 27.77537, 28.07577, 27.214415, 
    19.976595, 28.24768, 27.87019, 27.87417, 27.512335, 28.1487, 
    18.87582, 27.808485, 16.88221, 27.304755, 28.22269, 27.85485, 
    25.751105, 22.085525, 26.402235, 27.96385, 28.12362, 23.3511, 
    28.13134, 28.13854, 22.55042, 23.483435, 27.99603, 22.988955, 
    27.479825, 24.074565, 28.06897, 28.300515, 28.08899, 26.850145, 
    28.21175, 23.961985, 28.148655, 25.25804, 28.084975, 28.15017, 
    28.142015, 27.74106, 27.89856, 27.81585, 27.762185, 26.34704, 
    23.61241, 26.9402, 24.945815, 27.660965, 28.20952, 18.388425, 
    28.03487, 27.44823, 24.182, 20.80328, 28.138395, 26.294595, 
    27.8952, 24.294155, 26.569235, 28.076645, 21.003065, 28.221055, 
    25.336215, 28.17753, 27.934355, 25.886285, 28.10892, 26.890965, 
    28.06394, 27.89246, 25.030615, 27.71521, 27.994955, 27.091235, 
    26.189505, 17.829845, 26.78325, 27.566845, 21.749355, 28.181235, 
    28.216485, 17.523135, 28.145175, 28.05206, 28.198345, 27.46945, 
    25.099975, 27.266245, 27.54595, 26.236675, 28.27499, 28.230255, 
    21.91532, 25.631615, 25.69132, 26.62857, 27.83423, 23.119855, 
    28.08352, 15.10413, 27.84174, 28.10417, 20.39175, 19.55529, 
    27.62871, 19.76947, 27.87254)), row.names = c(39410L, 39411L, 
39412L, 39413L, 39414L, 39415L, 39416L, 39417L, 39418L, 39419L, 
39420L, 39421L, 39422L, 39423L, 39424L, 39425L, 39426L, 39427L, 
39428L, 39429L, 39430L, 39431L, 39432L, 39433L, 39434L, 39435L, 
39436L, 39437L, 39438L, 39439L, 39440L, 39441L, 39442L, 39443L, 
39444L, 39445L, 39446L, 39447L, 39448L, 39449L, 39450L, 39451L, 
39452L, 39453L, 39454L, 39455L, 39456L, 39457L, 39458L, 39459L, 
39460L, 39461L, 39462L, 39463L, 39464L, 39465L, 39466L, 39467L, 
39468L, 39469L, 39470L, 39471L, 39472L, 39473L, 39474L, 39475L, 
39476L, 39477L, 39478L, 39479L, 39480L, 39481L, 39482L, 39483L, 
39484L, 39485L, 39486L, 39487L, 39488L, 39489L, 39490L, 39491L, 
39492L, 39493L, 39494L, 39495L, 39496L, 39497L, 39498L, 39499L, 
39500L, 39501L, 39502L, 39503L, 39504L, 39505L, 39506L, 39507L, 
39508L, 39509L, 39510L, 39511L, 39512L, 39513L, 39514L, 39515L, 
39516L, 39517L, 39518L, 39519L, 39520L, 39521L, 39522L, 39523L, 
39524L, 39525L, 39526L, 39527L, 39528L, 39529L, 39530L, 39531L, 
39532L, 39533L, 39534L, 39535L, 39536L, 39537L, 39538L, 39539L, 
39540L, 39541L, 39542L, 39543L, 39544L, 39545L, 39546L, 39547L, 
39548L, 39549L, 39550L, 39551L, 39552L, 39553L, 39554L, 39555L, 
39556L, 39557L, 39558L, 39559L, 39560L, 39561L, 39562L, 39563L, 
39564L, 39565L, 39566L, 39567L, 39568L, 39569L, 39570L, 39571L, 
39572L, 39573L, 39574L, 39575L, 39576L, 39577L, 39578L, 39579L, 
39580L, 41926L, 41927L, 41928L, 41929L, 41930L, 41931L, 41932L, 
41933L, 41934L, 41935L, 41936L, 41937L, 41938L, 41939L, 41940L, 
41941L, 41942L, 41943L, 41944L, 41945L, 41946L, 41947L, 41948L, 
41949L, 41950L, 41951L, 41952L, 41953L, 41954L, 41955L, 41956L, 
41957L, 41958L, 41959L, 41960L, 41961L, 41962L, 41963L, 41964L, 
41965L, 41966L, 41967L, 41968L, 41969L, 41970L, 41971L, 41972L, 
41973L, 41974L, 41975L, 41976L, 41977L, 41978L, 41979L, 41980L, 
41981L, 41982L, 41983L, 41984L, 41985L, 41986L, 41987L, 41988L, 
41989L, 41990L, 41991L, 41992L, 41993L, 41994L, 41995L, 41996L, 
41997L, 41998L, 41999L, 42000L, 42001L, 42002L, 42003L, 42004L, 
42005L, 42006L, 42007L, 42008L, 42009L, 42010L, 42011L, 42012L, 
42013L, 42014L, 42015L, 42016L, 42017L, 42018L, 42019L, 42020L, 
42021L, 42022L, 42023L, 42024L, 42025L, 42026L, 42027L, 42028L, 
42029L, 42030L, 42031L, 42032L, 42033L, 42034L, 42035L, 42036L, 
42037L, 42038L, 42039L, 42040L, 42041L, 42042L, 42043L, 42044L, 
42045L, 42046L, 42047L, 42048L, 42049L, 42050L, 42051L, 42052L, 
42053L, 42054L, 42055L, 42056L, 42057L, 42058L, 42059L, 42060L, 
42061L, 42062L, 42063L, 42064L, 42065L, 42066L, 42067L, 42068L, 
42069L, 42070L, 42071L, 42072L, 42073L, 42074L, 42075L, 42076L, 
42077L, 42078L, 42079L, 42080L, 42081L, 42082L, 42083L, 42084L, 
42085L, 42086L, 42087L, 42088L, 42089L, 42090L, 42091L, 42092L, 
42093L, 42094L, 42095L, 42096L, 42097L, 42098L, 42099L, 42100L, 
42101L, 42102L, 42103L, 42104L, 42105L, 42106L, 42107L, 42108L, 
42109L, 42110L, 42111L, 42112L, 42113L, 42114L, 42115L, 42116L, 
42117L, 42118L, 42119L, 42120L, 42121L, 42122L, 42123L, 42124L, 
42125L, 42126L, 42127L, 42128L, 42129L, 42130L, 42131L, 42132L, 
42133L, 42134L, 42135L, 42136L), class = "data.frame")

Моя проблема на самом деле очень похожа на что этот человек имел в python Удаляет скачки как пики и шаги во временных сериях , но я не смог найти что-то похожее для R

РЕДАКТИРОВАТЬ: Я действительно нашел что-то похожее на R- связанный в этом вопросе https://stats.stackexchange.com/questions/139660/detecting-changes-in-time-series-r-example?newreg=f119230044de4802a9f0f6f4e4637d8f Решение с использованием tsoutliers выглядит применимо к моей проблеме, но пока оно работает не очень хорошо. Я не уверен в том, что я делаю неправильно. 

tso(dat.ts, types = c("LS","TC"))

полностью выравнивает мою кривую 

tso(dat.ts, types = c("LS","TC"), discard.method = "bottom-up")

Перемещает мою кривую в правильном направлении, но недостаточно для решения проблемы .

Любой подход, использующий восходящий метод для отбрасывания выбросов, дает такой результат, любой подход c, использующий массовый подход, выравнивает кривую.

1 Ответ

0 голосов
/ 14 февраля 2020

В приведенном ниже коде предпринята попытка нескольких методов, чтобы зафиксировать изменения: по процентному изменению и с использованием скользящего медианного метода.

Сначала импортируйте пару полезных библиотек. Также изменив ваши данные в таблицу под названием «lizard_data» 

library(tidyverse)
library(RcppRoll)
lizard_data <- tibble(your_data)

Затем, используя ggplot2, мы можем визуализировать все временные ряды. 

lizard_data %>% 
  pivot_longer(names_to = 'key', values_to = 'value', cols = c(LIZ, COP, BLK)) %>% 
  arrange(TIME) %>% 
  ggplot(aes(x = TIME, y = value, color = key))+
    geom_line()+
  facet_wrap(~ID, nrow = 2)

ggplot of initial data

Затем мы удаляем корректировки, находя дельту, и мы можем использовать два разных метода для сглаживания временных рядов. Во-первых, с помощью метода процентилей (изменение верхних 5% и нижних 5% значений на медиану), а во-вторых, с скользящей медианой (если максимальное изменение на десять процентов выше или ниже медианы, заменив значение медианой). ). 

ld_w_change <- lizard_data %>% 
  pivot_longer(names_to = 'key', values_to = 'value', cols = c(LIZ, COP, BLK)) %>% 
  group_by(ID, key) %>% 
  arrange(TIME) %>% 
  mutate(lag = lag(value),
    raw_change = (value-lag),
    #using the percentile change method
    med_raw_change = median(raw_change, na.rm = T),
    q_05 = quantile(raw_change,.05, na.rm = T),
    q_95 = quantile(raw_change,.95, na.rm = T),
    adj_raw_change = if_else(raw_change > q_95 | raw_change < q_05, med_raw_change, raw_change),
    normalized_change = if_else(is.na(adj_raw_change), 0,adj_raw_change),
    initial_value = first(value),
    roll_raw_change = cumsum(normalized_change),
    new_value_pct = initial_value + roll_raw_change,
    # using the rolling median method
    rolling_median_change = roll_median(raw_change, n = 5, align = "right", na.rm = T, fill = NA),
    adj_median_change = case_when(raw_change > rolling_median_change*1.1 | raw_change < rolling_median_change*.9 ~ rolling_median_change, 
                                  is.na(rolling_median_change) & is.na(raw_change) ~ 0,
                                  T ~ raw_change),
    normalized_med_change = cumsum(adj_median_change),
    new_value_roll = initial_value + normalized_med_change
    )

Лично я предпочитаю медианную корректировку, она, похоже, сохраняет форму данных лучше, чем метод процентили. Чтобы сравнить эти методы визуально, мы можем построить их рядом друг с другом: 

ld_w_change %>% 
  pivot_longer(names_to = 'method', values_to = 'adjusted_temp', cols = c(new_value_pct, new_value_roll)) %>% 
  ggplot(aes(x = TIME, y =adjusted_temp, color = key))+
  geom_line()+
  facet_wrap(ID ~ method, nrow = 2)

ggplot of two solutions

Добро пожаловать на сайт PullRequest, где вы можете задавать вопросы и получать ответы от других членов сообщества.

Похожие темы

...