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时间序列平稳性检验

[英]Test for Stationarity in time series

I need to check second order stationarity of a time series of length 7320 (I have 1800 such time series).我需要检查长度为 7320 的时间序列的二阶平稳性(我有 1800 个这样的时间序列)。 These time series are displacement recorded at 1800 sites on a mountain.这些时间序列是在山上 1800 个地点记录的位移。 I tried using Priestley-Subba Rao in R : stationarity() .我尝试在 R 中使用 Priestley-Subba Rao : stationarity() For 1 time series out of 1800, I got these values:对于 1800 年中的 1 个时间序列,我得到了这些值:

p-value for T : 2.109424e-15 
p-value for I+R : 9.447661e-06 
p-value for T+I+R : 1.4099e-10 

Could you please tell me how to interpret it.你能告诉我如何解释它吗? All I know is if the p-value for T is 0, the null hypothesis of time series being stationary is rejected.我所知道的是,如果 T 的 p 值为 0,则拒绝时间序列平稳的原假设。 Also, for 2nd time series out of 1800, I got these values;另外,对于 1800 年中的第二个时间序列,我得到了这些值;

p-value for T : 0 
p-value for I+R : 1.458063e-09 
p-value for T+I+R : 0

Could you tell me how to differentiate between the two.你能告诉我如何区分这两者吗? Both the time series are from the same dataset.两个时间序列都来自同一数据集。 Also, is it possible that one time series is stationary and another is not, given the fact they are from the same site and recorded at the exact same time.此外,考虑到它们来自同一地点并在完全相同的时间记录,一个时间序列是否可能是静止的,而另一个不是。

I also tried Wavelet Spectrum Test in R: hwtos2() function.我还尝试了 R 中的小波谱测试: hwtos2()函数。 But this function takes the time-series length that are power of 2. Is there any other better test for looking at stationarity that does not limit with the length of time series?但是这个函数采用的时间序列长度是 2 的幂。有没有其他更好的测试来查看不受时间序列长度限制的平稳性?

The book "Nonstationarities in Hydrologic and Environmental Time Series" (Springer Ed.), at pag. “水文和环境时间序列中的非平稳性”一书(Springer Ed.),第 1 页。 119, provides a good explanation for interpreting those p-values within the Priestley-Subba Rao test. 119,为在 Priestley-Subba Rao 检验中解释这些 p 值提供了很好的解释。

In general, you may also take a look at:一般来说,你也可以看看:

https://www.stat.tamu.edu/~suhasini/test_papers/priestley_subbarao70.pdf https://www.stat.tamu.edu/~suhasini/test_papers/priestley_subbarao70.pdf

About other stationarity tests, you may have a look at "weakly.stationary()" function within "analytics" package and to the "costat" package whose info at:关于其他平稳性测试,您可以查看“analytics”包中的“weakly.stationary()”函数和“costat”包,其信息位于:

https://www.jstatsoft.org/article/view/v055i01 https://www.jstatsoft.org/article/view/v055i01

where there is a suggestion to handle non dyadic length (ie, 2^J for some natural number J) time series.哪里有处理非二元长度(即某些自然数 J 为 2^J)时间序列的建议。 At pag.在页。 5: 5:

"It should be made clear that this is not a limitation of wavelets per se, but of the computationally efficient algorithms used to compute the intended quantities. Data sets of other lengths can be handled by zero-padding or truncation" “应该明确的是,这不是小波本身的限制,而是用于计算预期数量的计算效率高的算法的限制。其他长度的数据集可以通过零填充或截断来处理”

Some interesting info at:一些有趣的信息:

https://arxiv.org/pdf/1603.06415.pdf https://arxiv.org/pdf/1603.06415.pdf

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