如何检测和量化时间序列中的结构性中断 (R)

机器算法验证 r 时间序列 方差 描述性统计 结构变化
2022-03-23 03:17:03

背景

因此,首先需要一些背景来衡量我可能拥有的理解水平。目前正在完成硕士论文,尽管我确实有基本的了解,但统计数据一直是其中可以忽略不计的一部分。我目前的问题让我怀疑我在实践中可以/应该做什么,越来越多的在线阅读和文学似乎适得其反。

我想达到什么目的?

因此,对于我的论文,我加入了一家公司,我试图回答的一般问题基本上是预测过程如何受到某些系统的实施的影响(这会影响用于预测过程的数据)。

期望的结果是理解:

  1. 有明显的变化吗?(例如统计证明)
  2. 变化有多大?(均值和方差)
  3. 在这个预测过程中哪些因素很重要(以及因素的影响如何从休息前 > 休息后变化)

为了回答 1 和 2,我获得了时间序列对象形式的历史数据(在这个阶段更多但无关紧要)。我使用的软件是R

数据

数据包含每天(2.5 年)的加权分数,表明预测过程执行得有多糟糕(与实际事件的偏差)。这个时间序列对象包含这 2.5 年中从一小时前到事件实际发生(1 小时间隔)发生的预测的加权分数(因此,该间隔的每一天都有一个加权分数)。同样,为其他时间间隔(例如 1-2、2-3 小时等)构建了多个时间序列。

myts1 <- structure(c(412.028462047, 468.938224875, 372.353242472, 662.26844965, 
                 526.872020535, 396.434818388, 515.597528222, 536.940884418, 642.878650146, 
                 458.935314286, 544.096691918, 544.378838523, 486.854043968, 478.952935122, 
                 533.171083451, 507.543369365, 475.992539251, 411.626822157, 574.256785085, 
                 489.424743512, 558.03917366, 488.892234577, 1081.570101272, 488.410996801, 
                 420.058151274, 548.43547725, 759.563191992, 699.857042552, 505.546581256, 
                 2399.735167563, 959.058553387, 565.776425823, 794.327364085, 
                 1060.096712241, 636.011672603, 592.842508666, 643.576323635, 
                 639.649884944, 420.788373053, 506.948276856, 503.484363746, 466.642585817, 
                 554.521681602, 578.44355769, 589.29487224, 636.837396631, 647.548662447, 
                 740.222655163, 391.545826142, 537.551842222, 908.940523615, 590.446686171, 
                 543.002925217, 1406.486794264, 1007.596435757, 617.098818856, 
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                 1398.136047241, 749.644445942, 639.958039461, 649.265606673, 
                 645.57852203, 577.862446744, 663.218073256, 593.034544803, 672.096591437, 
                 544.776355324, 720.242877214, 824.963939263, 596.581822515, 885.215989867, 
                 693.456405627, 552.170633931, 618.855329732, 1030.291011295, 
                 615.889921256, 799.498196448, 570.398558528, 680.670975027, 563.404802085, 
                 494.790365745, 756.684436338, 523.051238729, 535.502475619, 520.8344231, 
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                 673.342687695, 567.501447619, 602.473664361, 655.181508321, 593.662768316, 
                 617.830786992, 652.461315007, 496.505155747, 550.24687917, 588.952116381, 
                 456.603281447, 425.963966309, 454.729462342, 487.22846023, 613.269432488, 
                 474.916140657, 505.93051487, 536.401546008, 555.824475073, 509.429036303, 
                 632.232746263, 677.102831732, 506.605957979, 701.99882145, 499.770942819, 
                 555.599224002, 557.634152694, 448.693828549, 661.921921922, 447.00540349, 
                 561.194112634, 590.797954608, 590.739061378, 445.949400588, 725.589882976, 
                 480.650749378, 587.03144903, 483.054524693, 428.813155209, 540.609606719, 
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                 460.613845164, 534.329569431, 560.663080722, 660.799405665, 432.3134958, 
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                 687.470213886, 951.651918891, 589.611971045, 493.203713291, 431.966577408, 
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                 610.670142045, 566.392016015, 611.086310256, 603.256299175, 766.372982953, 
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                 624.733620842, 803.199038618, 839.637983048, 1278.286165347, 
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                 192.166911863, 214.174943222, 271.287900593, 224.675083031, 171.950208574, 
                 173.867031268, 139.260432794, 177.012491325, 171.268066406, 132.714578168, 
                 197.224558817, 152.561299656, 143.415562042), .Tsp = c(2016.3306010929, 
                                                                        2018.99909424358, 365), class = "ts")

处理到现在

现在我已经明白,对于问题 1,我可以对结构性中断进行测试,以确定中断是否以及何时发生(具有已知的中断日期)。为此,我在 R 中使用 strucchange 包并利用断点函数。

但是,我的主管也推荐了CUSUM (用于未知休息日期)测试。不确定这里最好的是什么?

编辑:

我看到 Andrew 的 supF 测试针对所有可能的中断进行 Chow 测试。如果 F(或 Chow)统计量的最大值变得太大,则拒绝。(发现 -对时间序列进行 chow 测试

使用 struccchange 获取休息日期的代码

library(strucchange)
test2 <- Fstats(myts1~1) #Gets a sequence of fstatistics for all possible 
# break points within the middle 70% of myts1
myts1.fs <- test2$Fstats #These are the fstats
bp.myts1 <- breakpoints(myts1~1) #Gets the breakpoint based on the F-stats
plot(myts1) #plots the series myts1
lines(bp.myts1) #plots the break date implied by the sup F test
bd.myts1 <- breakdates(bp.myts1) #Obtains the implied break data (2018.35, 
# referring to day 128 (0.35*365 = day number))
sctest(test2) #Obtains a p-value for the implied breakpoint
ci.myts1 <- confint(bp.myts1) #95% CI for the location break date
plot(myts1)
lines(ci.myts1) #This shows the interval around the estimated break date

使用它我可以获得中断日期和95% CI,这告诉我发生了中断。但是,由于公式为 myts1~1,因此此中断是均值,反映了对常数的回归。如果我理解正确,残差是myts1的贬值值,因此我正在研究平均值的变化。该图使用中断日期和置信区间可视化数据。

阴谋

问题

Q0:在开始这个分析之前,我想知道我是否应该关注这些预测误差是如何分布的以及如何针对某些特征进行校正?除了发生的中断和一些异常值之外,这似乎是一个相当稳定的过程。

Q1:如何计算方差的变化我可以想象方差的变化也可能发生在与平均值不同的时间点?说方差的中断也是均值的中断是否正确,但是平方贬值序列的均值中断?没有太多可以找到的。

Q2:鉴于我现在已经获得了均值和方差中断的充分证据,我该如何量化这种变化?例如,在休息日之后,方差已从 X 转移到 Y?是否像沿休息日期拆分时间序列并汇总有关这两个部分的统计数据一样简单?

Q3:如果我对其他时间间隔重新运行中断分析,我如何比较不同预测范围内均值和方差的变化如何演变。这又是对统计数据的简单总结,还是有一个测试来评估错误的不同程度?

补充 Q3:##

在创建这些时间序列时,会考虑到预测事件发生前 10 小时的预测误差。

以一天为例:将预测分成 1 小时的 bin(创建 10 个 bin),然后在每个 bin 内,将所有预测汇总为加权平均值(根据不同的变量加权)。这意味着对于每一天,每个 bin 有一个加权分数(总共 10 个)。

将其转换为我在这篇文章中提供的时间序列对象(myts1,涵盖最后一小时)会产生以下结果:一个时间序列,其中每个点对应于给定时间间隔内当天的加权平均值。基本上每个 bin 包含 975 个不同的天数,每个天数的平均加权值(纯历史数据)。

我对这部分的想法:我添加了一张图片,其中包含 10 个垃圾箱中的 9 个垃圾箱,这清楚地表明,随着时间的推移,中断变得不那么明显了。鉴于这 10 个时间序列,我为每个时间序列重新运行“Score-CUSUM”(均值/方差)检验。从那里可以确定该系统的效果在哪个时间变得“明显”(如均值/方差的绝对变化)并且从操作的角度来看是可用的。

在此处输入图像描述

Q3.1这样分析时间序列有意义吗?我认为我重新运行 SCORE-CUSUM 测试 10 次并不重要?
Q3.1分割休息时如何处理跨越 6 个月的 95% CI?(在 4 小时后的垃圾箱中找到)
Q3.2在比较这 10 个时间间隔的不同模型(错误)时,我是否应该关注?

我希望我的解释足够,如有必要可以提供更多信息。

编辑:我添加了一个列格式的 csv 文件(由 ; 分隔),这还包括每天发生的事件数,但是,绘制时似乎没有相关性。链接:https ://www.dropbox.com/s/5pilmn43bps9ss4/Data.csv?dl=0

EDIT2:应该补充一点,实际实施发生时间序列中的时间点 2018 年第 136 天左右。

EDIT3:将第 1 小时到第 2 小时的第二个预测间隔添加为 R 中 pastebin 中的 TS 对象: https ://pastebin.com/50sb4RtP (主帖字符限制)

2个回答

问题

Q0:时间序列看起来相当右倾,并且水平偏移伴随着尺度偏移。因此,我将分析日志而不是级别的时间序列,即使用乘法而不是加法错误。在日志中,似乎 AR(1) 模型在每个段中都运行良好。参见例如acf()休息pacf()前后。

pacf(log(window(myts1, end = c(2018, 136))))
pacf(log(window(myts1, start = c(2018, 137))))

Q1:对于没有均值中断的时间序列,您可以简单地使用平方(或绝对)残差并再次运行水平偏移测试。或者,您可以基于最大似然模型运行测试和断点估计,其中误差方差是除回归系数之外的另一个模型参数。这是 Zeileis等人。(2010,doi:10.1016/j.csda.2009.12.005)。相应的基于分数的 CUSUM 测试也可用,strucchange但断点估计在fxregime. 最后,在仅寻找均值和方差变化时没有回归变量的情况下,changepointR 包还提供了专用函数。

话虽如此,对于您发布的时间序列,最小二乘法(将方差视为令人讨厌的参数)似乎就足够了。见下文。

Q2:是的。我会简单地将单独的模型拟合到每个部分,并分析这些“像往常一样”的 Bai & Perron (2003, Journal of Applied Econometrics ) 也认为这是渐近地证明的,因为断点估计的收敛速度更快(率n而不是n)。

Q3:我不完全确定您在这里寻找什么。如果您想按顺序运行测试以监控传入的数据,那么您应该采用正式的监控方法。Zeileis等人也对此进行了讨论。(2010)。

分析代码片段:

将日志序列与其滞后相结合以进行后续回归。

d <- ts.intersect(y = log(myts1), y1 = lag(log(myts1), -1))

使用 supF 和基于分数的 CUSUM 测试进行测试:

fs <- Fstats(y ~ y1, data = d)
plot(fs)
lines(breakpoints(fs))

Fstats

sc <- efp(y ~ y1, data = d, type = "Score-CUSUM")
plot(sc, functional = NULL)

全球环境基金

这突出表明,在原始时间序列中可见的时间点,截距和自相关系数都发生了显着变化。方差也有一些波动,但在 5% 的水平上并不显着。

基于 BIC 的约会也清楚地找到了这个断点:

bp <- breakpoints(y ~ y1, data = d)
coef(bp)
##                       (Intercept)        y1
## 2016(123) - 2018(136)    3.926381 0.3858473
## 2018(137) - 2019(1)      3.778685 0.2845176

显然,均值下降,但自相关也略有下降。日志中的拟合模型是:

plot(log(myts1), col = "lightgray", lwd = 2)
lines(fitted(bp))
lines(confint(bp))

断点

然后可以通过以下方式将模型重新拟合到每个段:

summary(lm(y ~ y1, data = window(d, end = c(2018, 136))))
## Call:
## lm(formula = y ~ y1, data = window(d, end = c(2018, 136)))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73569 -0.18457 -0.04354  0.12042  1.89052 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.92638    0.21656   18.13   <2e-16 ***
## y1           0.38585    0.03383   11.40   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2999 on 742 degrees of freedom
## Multiple R-squared:  0.1491, Adjusted R-squared:  0.148 
## F-statistic: 130.1 on 1 and 742 DF,  p-value: < 2.2e-16

 

summary(lm(y ~ y1, data = window(d, start = c(2018, 137))))
## Call:
## lm(formula = y ~ y1, data = window(d, start = c(2018, 137)))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.43663 -0.13953 -0.03408  0.09028  0.99777 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.61558    0.33468   10.80  < 2e-16 ***
## y1           0.31567    0.06327    4.99  1.2e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2195 on 227 degrees of freedom
## Multiple R-squared:  0.09883,    Adjusted R-squared:  0.09486 
## F-statistic:  24.9 on 1 and 227 DF,  p-value: 1.204e-06

不想在原帖中添加太多信息,这里的回复是在这部分之后对@Achim Zeleis的回应:

“相应的基于分数的 CUSUM 测试也可用,strucchange但断点估计在fxregime

还有问题 3,措辞不佳(现已在原帖中更新):

“回复:Q3。这听起来确实像你想要一个“监控”或“顺序测试”程序,在不同社区的不同标签下提出了各种工具。统计过程控制或质量控制可能是另一个相关标签。“

阅读了的小插图fxregimestrucchange获得了一个breakdate估计。
https://cran.r-project.org/web/packages/fxregime/vignettes/CNY.pdf
https://cran.r-project.org/web/packages/fxregime/fxregime.pdf

与这部分相关的问题如下:
1. 如何将 fxregime 发现的一个中断日期估计转换为截距和自相关的变化?
2.我在获得这个中断估计时使用的逻辑/方法是否fxregime正确?
3. 我什至应该期待两个中断日期,还是截距和自动相关的变化都发生在同一日期?(例如,如果方差会在不同的休息日期发生变化,那么我会得到两个 - 三个不同的休息日期吗?
4. 原帖的问题 3 适用(更新)
5. 我是否应该关注@Irish 建议的季节性影响Stat (deleted answer)? 我假设只有当我想在之后建模时,而不是在中断测试期间?

myts1的分析代码片段:

将日志序列与其滞后相结合以进行后续回归。
d <- ts.intersect(y = log(myts1), y1 = lag(log(myts1), -1))

进行相同的“Score-CUSUM”测试:

sc <- efp(y ~ y1, data = d, type = "Score-CUSUM")
plot(sc, functional = NULL)

sc 的基于分数的 CUSUM 测试

中断日期估计使用fxregime
1. LWZ 和负对数似然图显示 1 的最佳中断数
2. 具有置信区间的中断日期表明在观察 744 处中断

bd <- fxregimes(y~y1, data = d)
plot(bd) #LWZ and Negative Log-Likelihood plot indicating optimal number of breakpoints is 1 (following vignette information)
ci <- confint(bd, level = 0.95)
ci #show confidence interval for break date(s)

##         Confidence intervals for breakpoints
##         of optimal 2-segment partition: 
##
## Call:
## confint.fxregimes(object = bd, level = 0.95)
##
## Breakpoints at observation number:
##  2.5 % breakpoints 97.5 %
## 1   742         744    746
##
## Corresponding to breakdates:
##     2.5 % breakpoints   97.5 %
## 1 2018.363    2018.369 2018.374

LWZ 和负对数-似然 bd

然后coef我可以从每个段中获得系数。

coef(bd)
## 
##                                       (Intercept) y1     (Variance)
## 2016.33334081892--2018.36895725728    3.926381 0.3858473 0.08969063
## 2018.37169698331--2018.99909424358    3.778685 0.2845176 0.04813337

从这里我会说方差也下降了很多,但不确定如何正确解释这一点,因为在 Score-CUSUM 测试中给出了单个中断日期估计和不显着性?

第 2 部分,与 OP 中的问题 3 相关

现在正如(更新的)原帖的Q3中提到的,有多个时间序列,下面的一个是连续975天的间隔1-2小时的预测,每天有一个加权平均分数。

myts2 的分析代码片段:
关于 Q0:重新评估时间序列。参考原始帖子中的第二张图片,右侧偏斜仍然有些明显,并且查看休息前后的acf()前后pacf()仍然表明 AR(1) 模型可以很好地工作(我认为,类似的图表)。

pacf(log(window(myts2, end = c(2018, 136))))
pacf(log(window(myts2, start = c(2018, 137))))

再次将日志序列与其滞后相结合以进行后续回归。
e <- ts.intersect(y = log(myts2), y1 = lag(log(myts2), -1))

“Score-CUSUM”测试:

sc2 <- efp(y ~ y1, data = e, type = "Score-CUSUM")
plot(sc2, functional = NULL)

sc2 的基于分数的 CUSUM 测试 与第一个时间序列类似,截距和自相关系数在原始时间序列中可见的时间点发生显着变化。然而,这一次在 5% 水平上的方差也有明显的波动,与截距和自相关的时间点不直接匹配。

中断日期估计使用fxregime
1. LWZ 和负对数似然图显示最佳中断次数 1 由于 LWZ 急剧下降和 NLL 在断点 1 之后的扭结。
2. 具有置信区间的中断日期表示观察中断736

d <- fxregimes(y~y1, data = d)
plot(bd) #LWZ and Negative Log-Likelihood plot indicating optimal number of breakpoints is 1 (following vignette information)
ci <- confint(bd, level = 0.95)
ci #show confidence interval for break date(s)
# Confidence intervals for breakpoints  

# of optimal 2-segment partition: 
#   
#   Call:
#   confint.fxregimes(object = bd1, level = 0.95)
# 
# Breakpoints at observation number:
#   2.5 % breakpoints 97.5 %
#   1   730         736    750
# 
# Corresponding to breakdates:
#   2.5 % breakpoints   97.5 %
#   1 2018.331    2018.347 2018.385
# > breakdates(ci)
# 2.5 % breakpoints   97.5 %
#   1 2018.331    2018.347 2018.385

LWZ 和负对数-似然 bd1
然后coef我可以从每个段中获得系数。

coef(bd1)
#                                       (Intercept) y1     (Variance)
# 2016.33334081892--2018.34703944906    3.853897 0.3985997 0.07925990
# 2018.34977917509--2018.99909424358    3.106076 0.4773263 0.04625951

为了评估 myts2(1-2hr 预测区间)的这一部分,方差下降了很多,但与 myts1 相比变化较小。此外,截距系数和自相关系数发生了明显变化。

另外这里的问题是应该如何解释?这个单一的休息日期估计如何反映在 Score-CUSUM 测试中直观地看到的休息时间?

*还看到该refit函数将拟合 fxregimes 函数的分段回归,该函数可用于比较 @Achim Zeileis 最后提到的。
那么是否可以跨时间序列(myts1-10)比较模型(Q3)?我假设只有当它们共享相同的规模时,才能将具有日志的模型与没有日志的模型进行比较。