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pandas.ewmapandas.ewmapandas. ewma (arg , com=None , span=None , halflife=None , min_periods=0 , freq=None ,adjust=True , how=None , ignore_na=False )

Exponentially-weighted moving average

Parameters: arg : Series, DataFramecom : float. optional

Center of mass: ,

span : float, optional Specify decay in terms of span,

halflife : float, optional Specify decay in terms of halflife,

min_periods : int, default 0 Minimum number of observations in window required to havea value (otherwise result is NA).

freq : None or string alias / date offset object, default=NoneFrequency to conform to before computing statistic

adjust : boolean, default TrueDivide by decaying adjustment factor in beginning periods to

account for imbalance in relative weightings (viewing EWMAas a moving average)

how : string, default ‘mean’ Method for down- or re-sampling

ignore_na : boolean, default FalseIgnore missing values when calculating weights; specify Trueto reproduce pre-0.15.0 behavior

Returns: y : type of input argument

Notes

Either center of mass, span or halflife must be specified

EWMA is sometimes specified using a “span” parameter s , we have that the decayparameter is related to the span as

where c is the center of mass. Given a span, the associated center of mass is

So a “20-day EWMA” would have center 9.5.

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When adjust is True (default), weighted averages are calculated using weights

(1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1.

When adjust is False, weighted averages are calculated recursively as:

weighted_average[0] = arg[0]; weighted_average[i] =(1-alpha)*weighted_average[i-1] + alpha*arg[i].

When ignore_na is False (default), weights are based on absolute positions. For example, the weights of x and y used in calculating the final weighted average of [x,None, y] are (1-alpha)**2 and 1 (if adjust is True), and (1-alpha)**2 and alpha (if adjustis False).

When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based onrelative positions. For example, the weights of x and y used in calculating the finalweighted average of [x, None, y] are 1-alpha and 1 (if adjust is True), and 1-alpha andalpha (if adjust is False).

More details can be found at http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions

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