foundryts.functions.polynomial_regression

foundryts.functions.polynomial_regression(max_degree=0, time_unit='ns', start=None, end=None)

Returns a function that computes the polynomial regression for a single time series.

Polynomial regression finds parameters of the best-fit polynomial curve of a specified degree over the points of the input time series. The polynomial is expressed as y = a0 + a1*x + a2*x^2 + ... + an*x^n, where the coefficients a0, a1, ..., an are determined by the regression.

Polynomial regression is useful when the relationship between the variables is more complex than a simple linear relationship.

• Parameters:
  • max_degree (int , optional) – The maximum degree of the polynomial to fit (default is 0). For example, a degree of 2 fits a quadratic polynomial.
  • 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 polynomial regression.
  • end (str | int | datetime.datetime , optional) – End point (exclusive) of the time series for computing the polynomial regression.
• Returns: A function that accepts a single time series and returns parameters for the best-fit polynomial curve for the points in the time series using polynomial regression.
• 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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>>> poly_regr = F.polynomial_regression(3)(series)
>>> poly_regr.to_pandas()
   max_bounds.first_value  max_bounds.second_value  min_bounds.first_value  min_bounds.second_value  regression_fit_function.polynomial_regression_fit.coefficients.coefficient  regression_fit_function.polynomial_regression_fit.coefficients.degree
0                    50.0                     96.0                    10.0                      6.0                                          -4.800000                                                                           0
1                    50.0                     96.0                    10.0                      6.0                                           1.585714                                                                           1
2                    50.0                     96.0                    10.0                      6.0                                          -0.066429                                                                           2
3                    50.0                     96.0                    10.0                      6.0                                           0.001500                                                                           3