Question

我有一个时间序列数据集，每个时间序列都有来自不同/相同物种的30年数据点。我正在使用每个时间序列数据点的前23年数据来开发预测模型，并使用剩下的7年作为测试集来了解模型的预测能力，但是非线性模型（模型6和模型7）却无法做到。不能给出简洁的结果？

数据：

DD <- structure(list(Plot = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", 
"B", "C", "D"), class = "factor"), Species = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L), .Label = c("BD", "BG"), class = "factor"), Year = c(37L, 
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 
47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 
60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 
56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 
65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 
61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 
44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 37L, 38L, 39L, 
40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 
53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 
66L), Count = c(81L, 45L, 96L, 44L, 24L, 8L, 28L, 32L, 39L, 29L, 
40L, 17L, 4L, 12L, 18L, 11L, 63L, 98L, 78L, 76L, 67L, 36L, 56L, 
43L, 81L, 8L, 14L, 20L, 25L, 19L, 135L, 91L, 171L, 88L, 59L, 
1L, 1L, 1L, 2L, 1L, 11L, 9L, 34L, 15L, 32L, 21L, 33L, 43L, 39L, 
20L, 6L, 3L, 9L, 9L, 28L, 16L, 15L, 2L, 1L, 1L, 34L, 16L, 19L, 
35L, 32L, 7L, 2L, 30L, 29L, 25L, 28L, 11L, 31L, 31L, 28L, 27L, 
34L, 110L, 87L, 103L, 72L, 19L, 46L, 43L, 107L, 32L, 26L, 31L, 
12L, 29L, 23L, 40L, 50L, 23L, 34L, 11L, 9L, 4L, 24L, 55L, 14L, 
16L, 51L, 43L, 2L, 13L, 8L, 96L, 52L, 118L, 32L, 1L, 8L, 17L, 
34L, 29L, 38L, 15L, 4L, 38L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
4L, 6L, 4L, 4L, 10L, 6L, 7L, 9L, 15L, 30L, 25L, 36L, 13L, 17L, 
43L, 36L, 60L, 50L, 26L, 13L, 13L, 27L, 18L, 56L, 96L, 16L, 54L, 
2L, 2L, 9L, 5L, 5L, 6L, 2L, 6L, 2L, 3L, 4L, 3L, 136L, 71L, 116L, 
28L, 23L, 76L, 64L, 98L, 58L, 26L, 13L, 13L, 13L, 18L, 19L, 24L, 
18L, 17L, 3L, 23L, 19L, 9L, 11L, 13L, 20L, 29L, 29L, 17L, 20L, 
26L, 71L, 63L, 53L, 54L, 20L, 22L, 18L, 93L, 50L, 18L, 12L, 12L, 
31L), LogCount = c(1.908385019, 1.653212514, 1.982271233, 1.643462676, 
1.380211242, 0.903089987, 1.447158031, 1.505109978, 1.591064607, 
1.462397998, 1.602059991, 1.230448921, 0.602059991, 1.079181206, 
1.255272505, 1.041392685, 1.799340549, 1.991226076, 1.892094603, 
1.880813592, 1.826074803, 1.556302501, 1.748188027, 1.633468456, 
1.908485019, 0.903089987, 1.146128035, 1.301029996, 1.397940009, 
1.278753601, 2.130333768, 1.95904139, 2.2329961, 1.94448267, 
1.770852012, 0, 0, 0, 0.30102999, 0, 1.0411392685, 0.954242509, 
1.531478917, 1.176031259, 1.505149978, 1.322219295, 1.51851394, 
1.6334684456, 1.591064607, 1.301029996, 0.77815125, 0.477121255, 
0.954242509, 0.954242509, 1.447158031, 1.204119983, 1.176091259, 
0.301029996, 0, 0, 1.531478917, 1.204119983, 1.278753501, 1.544068044, 
1.505149978, 0.084509804, 0.301029996, 1.477121255, 1.462397998, 
1.397940009, 1.447158031, 1.041392685, 1.491361694, 1.491361694, 
1.447158031, 1.431363754, 1.531478917, 2.041392685, 1.939519253, 
2.012837225, 1.857332495, 1.278753601, 1.662757382, 1.633468456, 
2.029383778, 1.505149978, 1.414973348, 1.491361594, 1.079181245, 
1.462397998, 1.361727835, 1.602059991, 1.698970004, 1.361727836, 
1.531478917, 1.041392685, 0.954242509, 0.602059991, 1.380211242, 
1.740362689, 1.146128036, 1.204119983, 1.707570176, 1.633468456, 
0.301029996, 1.113943352, 0.903089987, 1.982271233, 1.716003344, 
2.071882007, 1.50514997, 0, 0.903089987, 1.230448921, 1.53147891, 
1.2397998, 1.57978359, 1.176091259, 0.602059991, 1.57978359, 
0.301029996, 0, 0, 0, 0, 0, 0.477121255, 0.477121255, 0.602059991, 
0.77815125, 0.602059991, 0.602059991, 1, 0.77815125, 0.84509804, 
0.95424509, 1.176091259, 1.477121255, 1.39790009, 1.555302501, 
1.113943352, 1.230448921, 1.633468456, 1.555302501, 1.77815125, 
1.698970004, 1.414973348, 1.113943352, 1.113943352, 1.431353754, 
1.255272505, 1.748188027, 1.982271233, 1.204119983, 1.73239376, 
1.431363754, 1.361727835, 0.954242509, 0.698970004, 0.698970004, 
0.77815125, 0.301029996, 0.77815125, 0.301029996, 0.477121255, 
0.602059991, 0.477121255, 2.133538908, 1.851258349, 2.064457989, 
1.447158031, 1.361727836, 1.880813592, 1.806179974, 1.991226076, 
1.763427994, 1.414973348, 1.113943352, 1.113943352, 1.113943352, 
1.255272505, 1.278753601, 1.380211242, 1.255272505, 1.230446921, 
0.477121255, 1.361727835, 1.278753601, 0.954242509, 1.0411392685, 
1.113943352, 1.301029996, 1.462397998, 1.462397998, 1.230448921, 
1.301029995, 1.414973348, 1.851258349, 1.799340549, 1.72427587, 
1.73239376, 1.301029996, 1.342422681, 1.255272505, 1.968482949, 
1.698970004, 1.255272505, 1.079181246, 1.079181246, 1.491361694
), Diff = c(-0.255272505, 0.329058719, -0.338818557, -0.263241434, 
-0.077121255, 0.544068044, 0.057991947, 0.085910629, -0.128666609, 
0.139661993, -0.37161107, -0.62838893, 0.477121255, 0.176091259, 
-0.21387982, 0.757947864, 0.191885527, -0.099131473, -0.011281011, 
-0.054738789, -0.269772302, 0.191885526, -0.114719571, 0.275016563, 
-1.005395032, 0.243038049, 0.15490196, 0.096910013, -0.119186408, 
NA, -0.171292376, 0.273954718, -0.288513438, -0.17363066, -1.770852012, 
0, 0, 0.301029996, -0.301029996, 1.041392685, -0.087150176, 0.577235408, 
-0.355387658, 0.329058719, -0.182930683, 0.196294545, 0.110954516, 
-0.042403849, -0.290034611, -0.522878746, -0.301029995, 0.477121254, 
0, 0.492915522, -0.243038048, -0.028028724, -0.875061263, -0.301029996, 
0, 1.531078917, -0.32735893, 0.070633618, 0.265310043, -0.038918066, 
-0.660051938, -0.544068044, 1.176091259, -0.014723257, -0.064457989, 
0.049218022, -0.405765346, 0.449969009, 0, -0.044203663, -0.015794267, 
0.100115153, 0.509913768, -0.101873432, 0.073317972, -0.155504729, 
-0.578578895, 0.384054231, -0.029289376, 0.395915322, -0.5202338, 
-0.09017663, 0.076388346, -0.412180448, 0.383216752, -0.100670162, 
0.240332155, 0.096910013, -0.337242168, 0.169751081, -0.490086232, 
-0.087150176, -0.352182518, 0.778151251, 0.360151447, -0.594234653, 
0.057991947, 0.503450193, -0.07410172, -1.33243846, 0.812913356, 
-0.210853365, 1.079181246, -0.266267889, 0.355878663, -0.566732029, 
-1.505149978, 0.903089987, 0.327358934, 0.301029996, -0.069080919, 
0.117385599, -0.403692338, -0.574031268, 0.977723606, -1.278753601, 
-0.301029996, 0, 0, 0, 0, 0.477121255, 0, 0.124938736, 0.176091259, 
-0.176091259, 0, 0.397490009, -0.2218485, 0.06690679, 0.10914469, 
0.22184875, 0.301029996, -0.079181206, 0.158362092, -0.442359149, 
0.116505569, 0.403019535, -0.077165955, 0.221848749, -0.079181206, 
-0.283996656, -0.301029996, 0, 0.317420412, -0.176091259, 0.492915522, 
0.23483206, -0.77815125, 0.528273777, -0.301029996, -0.069635928, 
-0.407485327, -0.255272505, 0, 0.079181246, -0.477121254, 0.477121254, 
-0.477121254, 0.176091259, 0.124938736, -0.124938736, 1.656417653, 
-0.282280559, 0.21319964, -0.617299958, -0.085430195, 0.5191085756, 
-0.074533518, 0.185045102, -0.227798082, -0.348454546, -0.301029996, 
0, 0, 0.141329153, 0.023481096, 0.101457641, -0.124938737, -0.024823584, 
-0.753327666, 0.884606581, -0.082974235, -0.324511092, 0.087150176, 
0.072550667, 0.187086644, 0.161368002, 0, -0.231949077, 0.070581075, 
0.113903352, 0.436285001, -0.00519178, -0.075054679, 0.00811789, 
-0.431363764, 0.041392685, -0.087150176, 0.713210444, -0.269512945, 
-0.443697499, -0.176091259, 0, 0.412180448, -0.148939013)), class = "data.frame", row.names = c(NA, 
-210L))

代码：

for(f in 1:11){
  for(b in 1:5){
    for (c in 1:5){

      #To select test sets 1,2,3,4, and 5 years beyond the training set:

      #Calculate the mean of abundance for the training set years.
      Model1<-lm(mean~1, data=DD1)

      #

Output2：

2 3 0.676209994477288 1.9365051784348e-09 4.44089209850063e-16
3 53 11.9236453578109 2.06371097988267e-09 1.13686837721616e-13
4 31 1.94583877614293 1.11022302462516e-15 1.99840144432528e-15
5 4 8.06660449042397 1.48071350736245e-08 3.19744231092045e-14
6 5 10.5321102149558 9.31706267692789e-10 1.4210854715202e-14

..

Answer 1

首先，请在下面查看不同物种和地块计数的时间序列图。

library(ggplot2)
ggplot(DD, aes(Year, Count)) +
  geom_point() +
  geom_line() +
  facet_grid(Plot ~ Species) + 
  scale_y_log10()

可以看出，没有明显的趋势可以使用nls通过幂或对数幂函数来拟合。

第二，据我了解，您正在尝试使用nls来预测训练数据集以外的内容。通常，由于时间序列具有自动相关性，因此使用最小二乘模型并不是十分有效。

第三，最简单的预测算法是Holt-Winters（请参见下面的“脏”实现）。您还可以使用大量其他算法，例如ARIMA，指数平滑状态空间等。

x <- ts(DD[DD$Species == "BG" & DD$Plot == "elq1a3", ]$LogCount)
m <- HoltWinters(x, gamma = FALSE)
library(forecast)
f <- forecast(m, 2)
plot(f, main = "elq1a3 at BG")

第四，关于您所讨论的算法，它会抛出

qr.solve（QR.B，cc）中的错误：求解中的奇异矩阵'a'。

原因是在for循环的第一步（f = b = c = 1 DD2数据帧仅包含一行。并执行

Model6<-nls(Diff~1+Count^T,start=list(T=1),trace=TRUE,algorithm ="plinear",data=DD2)

表示您试图仅使用一个数据点来拟合曲线，这是不可能的。但是，如果将f循环中的for值从1:11更改为2:11，则会引发另一个错误：

nls中的错误（Diff〜1 + Count ^ T，start = list（T = 1），trace = TRUE，算法=“ plinear”，：降低0.000488281以下 minFactor 0.000976562。

在这种情况下，您不能将plinear算法所使用的“天真”方法用于具有自动起始的初始值，例如nls.control(min.factor = 1e-5)。您必须使用默认的Gauss-Newton算法明确地输入所有初始系数。非常赞叹，请尝试一下：）

非线性模型收敛

1 个答案: