polars.Expr.ewm_std

Expr.ewm_std(
    *,
    com: float | None = None,
    span: float | None = None,
    half_life: float | None = None,
    alpha: float | None = None,
    adjust: bool = True,
    bias: bool = False,
    min_samples: int = 1,
    ignore_nulls: bool = False,
) → Expr

Compute exponentially-weighted moving standard deviation.

engine:In Memory

Changed in version 1.21.0: The min_periods parameter was renamed min_samples.

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 default) 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_samples

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

ignore_nulls

Ignore missing values when calculating weights.

• When ignore_nulls=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
• When ignore_nulls=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> df.select(pl.col("a").ewm_std(com=1, ignore_nulls=False))
shape: (3, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ null     │
│ 0.707107 │
│ 0.963624 │
└──────────┘