# featuretools.primitives.ExponentialWeightedVariance#

class featuretools.primitives.ExponentialWeightedVariance(com=None, span=None, halflife=None, alpha=None, ignore_na=False)[source]#

Computes the exponentially weighted moving variance for a series of numbers

Description:

Returns the exponentially weighted moving variance for a series of numbers. Exactly one of center of mass (com), span, half-life, and alpha must be provided. Missing values can be ignored when calculating weights by setting ‘ignore_na’ to True.

Parameters:
• com (float) – Specify decay in terms of center of mass for com >= 0. Default is None.

• span (float) – Specify decay in terms of span for span >= 1. Default is None.

• halflife (float) – Specify decay in terms of half-life for halflife > 0. Default is None.

• alpha (float) – Specify smoothing factor alpha directly. Alpha should be greater than 0 and less than or equal to 1. Default is None.

• ignore_na (bool) – Ignore missing values when calculating weights. Default is False.

Examples

```>>> exponential_weighted_variance = ExponentialWeightedVariance(com=0.5)
>>> exponential_weighted_variance([1, 2, 3, 4]).tolist()
[nan, 0.49999999999999983, 0.8461538461538459, 1.1230769230769233]
```

Missing values can be ignored

```>>> ewmv_ignorena = ExponentialWeightedVariance(com=0.5, ignore_na=True)
>>> ewmv_ignorena([1, 2, 3, None, 4]).tolist()
[nan, 0.49999999999999983, 0.8461538461538459, 0.8461538461538459, 1.1230769230769233]
```
__init__(com=None, span=None, halflife=None, alpha=None, ignore_na=False)[source]#

Methods

 `__init__`([com, span, halflife, alpha, ignore_na]) `flatten_nested_input_types`(input_types) Flattens nested column schema inputs into a single list. `generate_name`(base_feature_names) `generate_names`(base_feature_names) `get_args_string`() `get_arguments`() `get_description`(input_column_descriptions[, ...]) `get_filepath`(filename) `get_function`()

Attributes

 `base_of` `base_of_exclude` `commutative` `compatibility` Additional compatible libraries `default_value` Default value this feature returns if no data found. `description_template` `input_types` woodwork.ColumnSchema types of inputs `max_stack_depth` `name` Name of the primitive `number_output_features` Number of columns in feature matrix associated with this feature `return_type` ColumnSchema type of return `series_library` `stack_on` `stack_on_exclude` `stack_on_self` `uses_calc_time` `uses_full_dataframe`