Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models

Details Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models

2008-09-12

arxiv.org/abs/0809.2229v1

Nonlinear Sciences

Chaotic Dynamics

4 pages, 3 figures, 1 table, proceedings of NOLTA2008 Conference

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Turning Point Prediction of Oscillating Time Series using Local Dynamic Regression Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-369427