We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach combines Bayesian trend filtering and machine learning based regularization. The proposed decoupling approach combines the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators to provide changepoint analysis in complex, modern settings.