Approximation of data using non-integer harmonics series

Approximation of data using non-integer harmonics series This paper introduces a method for approximating real world data by means of non-integer harmonics series (NIHS). The NIHS terms are sinusoidal functions of arbitrary amplitude, frequency and phase shift that can be computed by direct numerical algorithms. Applications go from obtaining time derivatives up to forecasting future values. Three illustrative examples with the Dow Jones Industrial stock market index, the Europe Brent Spot Price FOB and the daily temperature records of New York city are studied. The results show the effectiveness of the NIHS when dealing with real world time data series. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

Approximation of data using non-integer harmonics series

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3629-4
Publisher site
See Article on Publisher Site

Abstract

This paper introduces a method for approximating real world data by means of non-integer harmonics series (NIHS). The NIHS terms are sinusoidal functions of arbitrary amplitude, frequency and phase shift that can be computed by direct numerical algorithms. Applications go from obtaining time derivatives up to forecasting future values. Three illustrative examples with the Dow Jones Industrial stock market index, the Europe Brent Spot Price FOB and the daily temperature records of New York city are studied. The results show the effectiveness of the NIHS when dealing with real world time data series.

Journal

Nonlinear DynamicsSpringer Journals

Published: Jun 28, 2017

References

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