### Abstract

We propose criteria that define a trend for time series with inherent multi-scale features. We call this trend the tendency of a time series. The tendency is defined empirically by a set of criteria and captures the large-scale temporal variability of the original signal as well as the most frequent events in its histogram. Among other properties, the tendency has a variance no larger than that of the original signal; the histogram of the difference between the original signal and the tendency is as symmetric as possible; and with reduced complexity, the tendency captures essential features of the signal. To find the tendency we first use the intrinsic time-scale decomposition (ITD) of the signal, introduced in 2007 by Frei and Osorio, to produce a set of candidate tendencies. We then apply the criteria to each of the candidates to single out the one that best agrees with them. While the criteria for the tendency are independent of the signal decomposition scheme, it is found that the ITD is a simple and stable methodology, well suited for multi-scale signals. The ITD is a relatively new decomposition and little is known about its outcomes. In this study we take the first steps towards a probabilistic model of the ITD analysis of random time series. This analysis yields details concerning the universality and scaling properties of the components of the decomposition.

Original language | English (US) |
---|---|

Article number | 085004 |

Journal | New Journal of Physics |

Volume | 16 |

DOIs | |

State | Published - 2014 |

### Fingerprint

### Keywords

- empirical model decomposition
- intrinsic time-scale decomposition
- multi-scale
- non-parametrtic
- non-stationary
- tendency
- time series
- trend

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*New Journal of Physics*,

*16*, [085004]. https://doi.org/10.1088/1367-2630/16/8/085004

**Defining a trend for time series using the intrinsic time-scale decomposition.** / Restrepo, Juan M.; Venkataramani, Shankar C; Comeau, Darin; Flaschka, Hermann.

Research output: Contribution to journal › Article

*New Journal of Physics*, vol. 16, 085004. https://doi.org/10.1088/1367-2630/16/8/085004

}

TY - JOUR

T1 - Defining a trend for time series using the intrinsic time-scale decomposition

AU - Restrepo, Juan M.

AU - Venkataramani, Shankar C

AU - Comeau, Darin

AU - Flaschka, Hermann

PY - 2014

Y1 - 2014

N2 - We propose criteria that define a trend for time series with inherent multi-scale features. We call this trend the tendency of a time series. The tendency is defined empirically by a set of criteria and captures the large-scale temporal variability of the original signal as well as the most frequent events in its histogram. Among other properties, the tendency has a variance no larger than that of the original signal; the histogram of the difference between the original signal and the tendency is as symmetric as possible; and with reduced complexity, the tendency captures essential features of the signal. To find the tendency we first use the intrinsic time-scale decomposition (ITD) of the signal, introduced in 2007 by Frei and Osorio, to produce a set of candidate tendencies. We then apply the criteria to each of the candidates to single out the one that best agrees with them. While the criteria for the tendency are independent of the signal decomposition scheme, it is found that the ITD is a simple and stable methodology, well suited for multi-scale signals. The ITD is a relatively new decomposition and little is known about its outcomes. In this study we take the first steps towards a probabilistic model of the ITD analysis of random time series. This analysis yields details concerning the universality and scaling properties of the components of the decomposition.

AB - We propose criteria that define a trend for time series with inherent multi-scale features. We call this trend the tendency of a time series. The tendency is defined empirically by a set of criteria and captures the large-scale temporal variability of the original signal as well as the most frequent events in its histogram. Among other properties, the tendency has a variance no larger than that of the original signal; the histogram of the difference between the original signal and the tendency is as symmetric as possible; and with reduced complexity, the tendency captures essential features of the signal. To find the tendency we first use the intrinsic time-scale decomposition (ITD) of the signal, introduced in 2007 by Frei and Osorio, to produce a set of candidate tendencies. We then apply the criteria to each of the candidates to single out the one that best agrees with them. While the criteria for the tendency are independent of the signal decomposition scheme, it is found that the ITD is a simple and stable methodology, well suited for multi-scale signals. The ITD is a relatively new decomposition and little is known about its outcomes. In this study we take the first steps towards a probabilistic model of the ITD analysis of random time series. This analysis yields details concerning the universality and scaling properties of the components of the decomposition.

KW - empirical model decomposition

KW - intrinsic time-scale decomposition

KW - multi-scale

KW - non-parametrtic

KW - non-stationary

KW - tendency

KW - time series

KW - trend

UR - http://www.scopus.com/inward/record.url?scp=84907359675&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907359675&partnerID=8YFLogxK

U2 - 10.1088/1367-2630/16/8/085004

DO - 10.1088/1367-2630/16/8/085004

M3 - Article

AN - SCOPUS:84907359675

VL - 16

JO - New Journal of Physics

JF - New Journal of Physics

SN - 1367-2630

M1 - 085004

ER -