Higher-order rolling window statistics extending the core
pandas.Series.rolling()
API:
sem, skew, kurt, and
quantile.
The standard error of the mean measures how much the sample mean would vary across repeated samples. For a window of n values:
The standard error of the mean measures how much the sample mean would vary across repeated samples. For a window of n values:
Skewness measures asymmetry of the distribution in each window. Positive = right tail heavier; negative = left tail heavier. Uses the unbiased Fisher-Pearson formula (same as pandas):
Skewness measures asymmetry of the distribution in each window. Positive = right tail heavier; negative = left tail heavier. Uses the unbiased Fisher-Pearson formula (same as pandas):
Kurtosis measures how heavy the tails are relative to a normal distribution. The excess kurtosis subtracts 3, so a normal distribution gives 0. Uses the Fisher (1930) unbiased formula:
Kurtosis measures how heavy the tails are relative to a normal distribution. The excess kurtosis subtracts 3, so a normal distribution gives 0. Uses the Fisher (1930) unbiased formula:
Computes any quantile within each sliding window using configurable
interpolation. When q = 0.5 this is identical to
rolling.median().