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

2008/09/12 by Dimitris Kugiumtzis, D. Kugiumtzis, Kugiumtzis, D. +3
Computer Science · Physics and Astronomy · #Chaos control and synchronization #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Neural Networks and Applications #Nonlinear Dynamics and Pattern Formation #nlin.CD

paper · pdf · doi:10.48550/arxiv.0809.2229

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

arxiv created 2008/09/12 · openalex publication_date 2008/09/12 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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