foundryts.functions.exponential_regression

foundryts.functions.exponential_regression(include_multiple=True, time_unit='ns', start=None, end=None)

Returns a function that performs exponential regression on a single time series.

Exponential regression finds the parameters of the best-fit exponential curve over points of the input time series. The regression is expressed as y = Ae^(Bx), where A is the initial value and B is the growth rate. The returned function will provide the parameters A and B.

Exponential regression is particularly useful when the data exhibits exponential growth or decay patterns.

• Parameters:
  • include_multiple (bool , optional) – Whether to include multiple regressions (default is True).
  • time_unit (str , optional) – The time unit of the coefficients, must be one of “s”, “ms”, “us”, “ns” (default is “ns”).
  • start (str | int | datetime.datetime , optional) – Starting point (inclusive) of the time series for computing the exponential regression.
  • end (str | int | datetime.datetime , optional) – End point (exclusive) of the time series for computing the exponential regression.
• Returns: A function that accepts a single time series and provides parameters for the best-fit exponential curve for the time series.
• Return type: (FunctionNode) -> SummarizerNode

Dataframe schema

Examples

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>>> series = F.points(
...     (10, 6.0), (20, 12.0), (30, 24.0), (40, 48.0), (50, 96.0), name="series"
... )
>>> series.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000010    6.0
1 1970-01-01 00:00:00.000000020   12.0
2 1970-01-01 00:00:00.000000030   24.0
3 1970-01-01 00:00:00.000000040   48.0
4 1970-01-01 00:00:00.000000050   96.0

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>>> exponential_regr = F.exponential_regression()(series)
>>> exponential_regr.to_pandas()
   max_bounds.first_value  max_bounds.second_value  min_bounds.first_value  min_bounds.second_value  regression_fit_function.exponential_regression_fit.aparameter  regression_fit_function.exponential_regression_fit.bparameter
0                    50.0                     96.0                    10.0                      6.0                                                3.0                                                       0.069315