如何找出自相关和偏自相关中的滞后数？

Question

我正在尝试使用python中的tsfresh包，以便从加速度传感器输入中提取特征，我遇到了几个需要滞后变量的特征：

tsfresh.feature_extraction.feature_calculators.agg_autocorrelation(x, param)
Where:
                  x (numpy.ndarray) – the time series to calculate the feature of
                  param (list) – contains dictionaries {“f_agg”: x, “maxlag”, n} with x str, the name of a numpy function (e.g. “mean”, “var”, “std”, “median”), its the name of the aggregator function that is applied to the autocorrelations. Further, n is an int and the maximal number of lags to consider.

tsfresh.feature_extraction.feature_calculators.autocorrelation(x, lag)
Where:
                  x (numpy.ndarray) – the time series to calculate the feature of
                  lag (int) – the lag

tsfresh.feature_extraction.feature_calculators.c3(x, lag)
Where:
                  x (numpy.ndarray) – the time series to calculate the feature of
                  lag (int) – the lag

tsfresh.feature_extraction.feature_calculators.partial_autocorrelation(x, param)
Where:
                  x (numpy.ndarray) – the time series to calculate the feature of
                  param (list) – contains dictionaries {“lag”: val} with int val indicating the lag to be returned

tsfresh.feature_extraction.feature_calculators.time_reversal_asymmetry_statistic(x, lag)
Where:
                  x (numpy.ndarray) – the time series to calculate the feature of
                  lag (int) – the lag that should be used in the calculation of the feature

那么，我想知道如何找到滞后变量？

亲切的问候

Answer 1

TSFRESH使用滞后变量作为参数来计算相关特征。

例如，在c3计算中，它将使用lag=1，然后使用lag=2，这样做会将包含计算数据的列添加为tsXcolname__c3__lag_1。

您应该在TSFRESH中查找如何更改此参数，以了解它将为每个功能计算多少延迟。我认为c3的标准值是3，自相关的标准值是9。