  In the prediction of oscillating time series, the interest is in the turning
points of successive oscillations rather than the samples themselves. For this
purpose a scheme has been proposed; the state space reconstruction is limited
to the turning points and the local (nearest neighbor) model is modified in
order to predict the turning point magnitudes and times. This approach is
extended here using a local dynamic regression model on both turning point
magnitudes and times. Simulations on oscillating nonlinear systems show that
the proposed approach gives better predictions of turning points than the
standard local model applied to all the samples of the oscillating time series.
