brightwind.analyse.correlation.OrdinaryLeastSquares

class brightwind.analyse.correlation.OrdinaryLeastSquares(ref_spd, target_spd, averaging_prd='1H', coverage_threshold=0.9, preprocess=True)

Accepts two DataFrames with timestamps as indexes and averaging period.

Parameters

• ref_spd (pandas.Series) – Series containing reference speed as a column, timestamp as the index.

• target_spd (pandas.Series) – DataFrame containing target speed as a column, timestamp as the index.

• averaging_prd (string or pandas.DateOffset) –

  Groups data by the period specified by period.

  • 2T, 2 min for minutely average

  • Set period to 1D for a daily average, 3D for three hourly average, similarly 5D, 7D, 15D etc.

  • Set period to 1H for hourly average, 3H for three hourly average and so on for 5H, 6H etc.

  • Set period to 1MS for monthly average

  • Set period to 1AS fo annual average

• coverage_threshold (float) – Minimum coverage to include for correlation

• preprocess (bool) – To average and check for coverage before correlating

Returns

An object representing ordinary least squares fit model

__init__(ref_spd, target_spd, averaging_prd='1H', coverage_threshold=0.9, preprocess=True)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(ref_spd, target_spd[, …])	Initialize self.
get_error_metrics()
get_r2()	Returns the r2 score of the model
linear_func(p, x)
plot([title])	For plotting
run([show_params])
show_params()	Show the dictionary of parameters
synthesize([ext_input])