polars.Series.ewm_var#

Series.ewm_var(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, adjust: bool = True, bias: bool = False, min_periods: int = 1) Series[source]#

Exponentially-weighted moving variance.

Parameters:
com

Specify decay in terms of center of mass, \(\gamma\), with

\[\alpha = \frac{1}{1 + \gamma} \; \forall \; \gamma \geq 0\]
span

Specify decay in terms of span, \(\theta\), with

\[\alpha = \frac{2}{\theta + 1} \; \forall \; \theta \geq 1\]
half_life

Specify decay in terms of half-life, \(\lambda\), with

\[\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \lambda } \right\} \; \forall \; \lambda > 0\]
alpha

Specify smoothing factor alpha directly, \(0 < \alpha \leq 1\).

adjust

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

  • When adjust=True the EW function is calculated using weights \(w_i = (1 - \alpha)^i\)

  • When adjust=False the EW function is calculated recursively by

    \[\begin{split}y_0 &= x_0 \\ y_t &= (1 - \alpha)y_{t - 1} + \alpha x_t\end{split}\]
bias

When bias=False, apply a correction to make the estimate statistically unbiased.

min_periods

Minimum number of observations in window required to have a value (otherwise result is null).

Examples

>>> s = pl.Series("a", [1, 2, 3])
>>> s.ewm_var(com=1)
shape: (3,)
Series: 'a' [f64]
[
    0.0
    0.5
    0.928571
]