什么描述性统计通常用于时间序列数据？(What descriptive statistics are commonly used for time

什么描述性统计通常用于时间序列数据？(What descriptive statistics are commonly used for time-series data?)

我有一个每周使用数据的时间序列，我将尝试使用一些统计数据来分割人口。 偏斜和Kurtosis可以让我描述时间序列，并以不同的方式将人们分组。 但是我也注意到一些似乎有锯齿形图案或双峰模式，那么我不认为这两个上述统计数据会很好地描述它们。 与平均值的距离将告诉我谁持续稳定使用与不可预测的使用。

什么描述性统计通常用于时间序列数据？

谢谢，

I have a time-series of weekly usage data and I'm going to attempt to use some statistics to segment the population. Skewness and Kurtosis to may allow me to describe the time-series and group the people in different ways. But I also notice some appear to have saw-tooth patterns, or bimodal patterns, then I don't think these two aforementioned statistics will describe them well. Distance from the mean would tell me who has continual steady usage vs. unpredictable usage.

What descriptive statistics are commonly used for time-series data?

Thanks,

最满意答案

周期图和自相关函数是用于分析和建模时间序列的两种常见信息源。 您可以使用此信息来比较系列。

在周期图中，您可以检测估计的谱密度最高的频率。 这将告诉您哪个系列由相同频率的周期控制。

自相关函数（周期图的时域对应）和部分自相关函数可以类似地用于比较和分组序列。 那些在相同滞后阶段具有显着自相关的序列可以组合在一起。

您可能需要转换序列以识别某些信息，例如采用差异来使数据静止。 或者，您可以为每个系列选择ARIMA模型，并比较每个模型的特征（这些特征与自相关函数中观察到的特征几乎相同）。

The periodogram and the autocorrelation function are two common sources of information used to analyse and model time series. You can use this information to compare the series.

In the periodogram you can detect the frequencies at which the estimated spectral density is the highest. This will tell you which series are dominated by cycles of the same frequency.

The autocorrelation function (the time domain counterpart of the periodogram) and the partial autocorrelation function can similarly be used to compare and group the series. Those series with significant autocorrelations at the same lag orders could be grouped together.

You may need to transform the series in order to discern some of this information, for example taking differences to render the data stationary. Alternatively you can select an ARIMA model for each series and compare the characteristics of each model (those characteristics will be pretty much the same as those observed in the autocorrelation functions).