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Enhanced time series predictability with well-defined structures

  • Yu Huang
  • Zuntao FuEmail author
Original Paper
  • 52 Downloads

Abstract

For any given time series, how to optimize its forecast strategies and what prediction model is adopted are of great importance. In order to reach this goal, insight from analyzing predictability of series with known structure information is necessary. Time series generated by theoretical models with four kinds of known predictive structures, i.e., short-term correlation, long-term correlation, and multifractal and chaotic patterns, are applied to demonstrate that there is a well-defined relation between series’ intrinsic predictability and prediction accuracy of any specific prediction model. And results show that both intrinsic predictability and prediction accuracy are enhanced by these well-defined structures. There are different regimes in the relation between intrinsic predictability and prediction accuracy for series with different known deterministic or stochastic predictive structures. These regimes in the relation between intrinsic predictability and prediction accuracy can guide us to preselect a suitable prediction model and forecast strategies for any underlying series by only analyzing the permutation entropy of a given series. Results from three pieces of climate series further confirm that insights from theoretical series with known structure information indeed work well.

Notes

Funding information

This research was supported by the National Natural Science Foundation of China through grants (No. 41675049 and No. 41475048).

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Lab for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of PhysicsPeking UniversityBeijingChina

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