Class TripleExponentialMovingFeature

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Import

from NitroFE import TripleExponentialMovingFeature

TripleExponentialMovingFeature

The Triple exponential moving average (TEMA) was designed to smooth value fluctuations, thereby making it easier to identify trends without the lag associated with traditional moving averages (MA). It does this by taking multiple exponential moving averages (EMA) of the original EMA and subtracting out some of the lag.

TEMA is calculated as

\[ EMA_1 = Exponential \ moving \ average \ of \ dataframe \]

\[ EMA_2 = Exponential \ moving \ average \ of \ EMA_1 \]

\[ EMA_3 = Exponential \ moving \ average \ of \ EMA_2 \]

\[ TEMA = 3*EMA_1 - 3*EMA_2 + EMA_3 \]

Methods

Provided dataframe must be in ascending order.

`init(self, alpha=None, operation='mean', initialize_using_operation=False, initialize_span=None, com=None, span=None, halflife=None, min_periods=0, ignore_na=False, axis=0, times=None)` `special`

Parameters:

Name	Type	Description	Default
`alpha`	`float`	Specify smoothing factor directly, by default None	`None`
`operation`	`str`	operation to be performed for the moving feature,available operations are 'mean','var','std', by default 'mean'	`'mean'`
`initialize_using_operation`	`bool`	If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False	`False`
`initialize_span`	`int`	the span over which 'operation' would be performed for initialization, by default None	`None`
`com`	`float`	Specify decay in terms of center of mass, by default None	`None`
`span`	`float`	pecify decay in terms of span , by default None	`None`
`halflife`	`float`	Specify decay in terms of half-life, by default None	`None`
`min_periods`	`int`	Minimum number of observations in window required to have a value (otherwise result is NA)., by default 0	`0`
`ignore_na`	`bool`	Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False	`False`
`axis`	`int`	The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0	`0`
`times`	`str`	Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None	`None`

Source code in nitrofe\time_based_features\moving_average_features\moving_average_features.py

def __init__(
    self,
    alpha: float = None,
    operation: str = "mean",
    initialize_using_operation: bool = False,
    initialize_span: int = None,
    com: float = None,
    span: float = None,
    halflife: float = None,
    min_periods: int = 0,
    ignore_na: bool = False,
    axis: int = 0,
    times: str = None,
):
    """
    Parameters
    ----------
    alpha : float, optional
        Specify smoothing factor  directly, by default None
    operation : str, {'mean','var','std'}
        operation to be performed for the moving feature,available operations are 'mean','var','std', by default 'mean'
    initialize_using_operation : bool, optional
        If True, then specified 'operation' is performed on the first 'initialize_span' values, and then the exponential moving average is calculated, by default False
    initialize_span : int, optional
        the span over which 'operation' would be performed for initialization, by default None
    com : float, optional
        Specify decay in terms of center of mass, by default None
    span : float, optional
        pecify decay in terms of span , by default None
    halflife : float, optional
        Specify decay in terms of half-life, by default None
    min_periods : int, optional
        Minimum number of observations in window required to have a value (otherwise result is NA)., by default 0
    ignore_na : bool, optional
        Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior, by default False
    axis : int, optional
        The axis to use. The value 0 identifies the rows, and 1 identifies the columns, by default 0
    times : str, optional
        Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype, by default None
    """

    self.com = com
    self.span = span
    self.halflife = halflife
    self.alpha = alpha
    self.min_periods = min_periods
    self.ignore_na = ignore_na
    self.axis = axis
    self.times = times
    self.operation = operation
    self.initialize_using_operation = initialize_using_operation
    self.initialize_span = initialize_span

`fit(self, dataframe, first_fit=True)`

For your training/initial fit phase (very first fit) use fit_first=True, and for any production/test/subsequent implementation pass fit_first=False