### Abstract

The search for weak periodic signals in time series data is an active topic of research. Given the fact that rarely a real world dataset is perfectly periodic, this paper approaches this problem in terms of data mining, trying to discover weak periodic signals in time series databases, when no period length is known in advance. In existing time series mining algorithms, the period length is user-specified. We propose an algorithm for finding approximate periodicities in large time series data, utilizing autocorrelation function and FFT. This algorithm is an extension to the partial periodicity detection algorithm presented in a previous paper of ours. We provide some mathematical background as well as experimental results.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 51-61 |

Number of pages | 11 |

Volume | 2431 LNAI |

Publication status | Published - 1 Dec 2002 |

Externally published | Yes |

Event | 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002 - Helsinki, Finland Duration: 19 Aug 2002 → 23 Aug 2002 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 2431 LNAI |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002 |
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Country | Finland |

City | Helsinki |

Period | 19/8/02 → 23/8/02 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 2431 LNAI, pp. 51-61). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2431 LNAI).

**On the discovery of weak periodicities in large time series.** / Berberidis, Christos; Vlahavas, Ioannis; Aref, Walid G.; Atallah, Mikhail; Elmagarmid, Ahmed.

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

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 2431 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2431 LNAI, pp. 51-61, 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002, Helsinki, Finland, 19/8/02.

}

TY - GEN

T1 - On the discovery of weak periodicities in large time series

AU - Berberidis, Christos

AU - Vlahavas, Ioannis

AU - Aref, Walid G.

AU - Atallah, Mikhail

AU - Elmagarmid, Ahmed

PY - 2002/12/1

Y1 - 2002/12/1

N2 - The search for weak periodic signals in time series data is an active topic of research. Given the fact that rarely a real world dataset is perfectly periodic, this paper approaches this problem in terms of data mining, trying to discover weak periodic signals in time series databases, when no period length is known in advance. In existing time series mining algorithms, the period length is user-specified. We propose an algorithm for finding approximate periodicities in large time series data, utilizing autocorrelation function and FFT. This algorithm is an extension to the partial periodicity detection algorithm presented in a previous paper of ours. We provide some mathematical background as well as experimental results.

AB - The search for weak periodic signals in time series data is an active topic of research. Given the fact that rarely a real world dataset is perfectly periodic, this paper approaches this problem in terms of data mining, trying to discover weak periodic signals in time series databases, when no period length is known in advance. In existing time series mining algorithms, the period length is user-specified. We propose an algorithm for finding approximate periodicities in large time series data, utilizing autocorrelation function and FFT. This algorithm is an extension to the partial periodicity detection algorithm presented in a previous paper of ours. We provide some mathematical background as well as experimental results.

UR - http://www.scopus.com/inward/record.url?scp=84864839896&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864839896&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540440372

SN - 9783540440376

VL - 2431 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 51

EP - 61

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -