incline.local_polynomial_trend

incline.local_polynomial_trend(df, column_value='value', time_column=None, bandwidth=0.2, degree=2, kernel='gaussian', **kwargs)

Estimate the trend by local polynomial regression.

Linear given the bandwidth, so standard errors are exact and come from the same weighted least squares solve that produces the estimate.

Parameters:

• df (pd.DataFrame) – Time series data.

• column_value (str) – Column holding the values.

• time_column (str | None) – Numeric time column.

• bandwidth (float) – Kernel width as a fraction of the series span.

• degree (int) – Degree of the local polynomial.

• kernel (str) – 'gaussian', 'epanechnikov' or 'uniform'.

• **kwargs (Any) – Uncertainty options; see estimate().

Returns:

The input frame plus the estimate columns.

Return type:

pd.DataFrame