text: Title: [ENH] Add Spline Forecaster
  Body: **Is your feature request related to a problem? Please describe.**
  So far, when detrending, we can use linear regression with polynomial features. I would like something more flexible, i.e. fiting linear splines in a way that Prophet does it, but without the probabilistic programming overhead and with more control about what happens when extrapolating.
  
  **Describe the solution you'd like**
  I would like a new class like the `PolynomialTrendForecaster`, but `SplineTrendForecaster` or so. Internally, it would look like the `PolynomialTrendForecaster`, with (hopefully) the only difference being that instead of
  ```python
  self.regressor_ = make_pipeline(
      PolynomialFeatures(degree=self.degree, include_bias=self.with_intercept),
      regressor,
  )
  ```
  
  we would have something like
  
  ```python
  self.regressor_ = make_pipeline(
      SplineTransformer(degree=self.degree, n_knots=self.knots, extrapolation=self.extrapolation, include_bias=self.with_intercept),
      regressor,
  )
  ```
  
  We could maybe even write another class `PipelineTrendForecaster` that takes a scikit-learn pipeline. From that, we could easily derive `PolynomialTrendForecaster` and `SplineTrendForecaster`.
  
  scikit-learn's `SplineTransformer` has the advantage that you can tell it how to extrapolate, either linearly, constant, periodic, or more.