### Abstract

Calculating the fractal dimension of a time series can be useful in that it: (a) gives an indication as to how many state variables are influencing the process output, (b) can be used to reject a null hypothesis that the system is random, (c) can be used as a descriptive statistic, and (d) it may indicate that some short term prediction is possible. Existing methods for calculating fractal dimension are based on topological approaches. Experiments are shown here which instead utilize nonlinear time series methods to model dynamical systems, and estimate fractal dimension. Simulations of a simple production system indicate when such systems may be chaotic as opposed to random.

Original language | English (US) |
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Title of host publication | Industrial Engineering Research - Conference Proceedings |

Place of Publication | Norcross, GA, United States |

Publisher | IIE |

Pages | 1025-1032 |

Number of pages | 8 |

State | Published - 1995 |

Externally published | Yes |

Event | Proceedings of the 1995 4th Industrial Engineering Research Conference - Nashville, TN, USA Duration: May 24 1995 → May 25 1995 |

### Other

Other | Proceedings of the 1995 4th Industrial Engineering Research Conference |
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City | Nashville, TN, USA |

Period | 5/24/95 → 5/25/95 |

### Fingerprint

### ASJC Scopus subject areas

- Industrial and Manufacturing Engineering

### Cite this

*Industrial Engineering Research - Conference Proceedings*(pp. 1025-1032). Norcross, GA, United States: IIE.

**Estimating fractal dimension, with application to production systems.** / Dooley, Kevin.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Industrial Engineering Research - Conference Proceedings.*IIE, Norcross, GA, United States, pp. 1025-1032, Proceedings of the 1995 4th Industrial Engineering Research Conference, Nashville, TN, USA, 5/24/95.

}

TY - GEN

T1 - Estimating fractal dimension, with application to production systems

AU - Dooley, Kevin

PY - 1995

Y1 - 1995

N2 - Calculating the fractal dimension of a time series can be useful in that it: (a) gives an indication as to how many state variables are influencing the process output, (b) can be used to reject a null hypothesis that the system is random, (c) can be used as a descriptive statistic, and (d) it may indicate that some short term prediction is possible. Existing methods for calculating fractal dimension are based on topological approaches. Experiments are shown here which instead utilize nonlinear time series methods to model dynamical systems, and estimate fractal dimension. Simulations of a simple production system indicate when such systems may be chaotic as opposed to random.

AB - Calculating the fractal dimension of a time series can be useful in that it: (a) gives an indication as to how many state variables are influencing the process output, (b) can be used to reject a null hypothesis that the system is random, (c) can be used as a descriptive statistic, and (d) it may indicate that some short term prediction is possible. Existing methods for calculating fractal dimension are based on topological approaches. Experiments are shown here which instead utilize nonlinear time series methods to model dynamical systems, and estimate fractal dimension. Simulations of a simple production system indicate when such systems may be chaotic as opposed to random.

UR - http://www.scopus.com/inward/record.url?scp=0029424086&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029424086&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029424086

SP - 1025

EP - 1032

BT - Industrial Engineering Research - Conference Proceedings

PB - IIE

CY - Norcross, GA, United States

ER -