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.
Portions of this work were supported by Grant EIA-9903545 from the National Science Foundation, Contract N00014-02-1-0364 from the Office of Naval Research, and by sponsors of the Center for Education and Research in Information Assurance and Security.
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Keywords
- Period Length
- Periodic Pattern
- Fast Fourier Transform Algorithm
- Partial Periodicity
- Time Series Database
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Berberidis, C., Vlahavas, I., Aref, W.G., Atallah, M., Elmagarmid, A.K. (2002). On the Discovery of Weak Periodicities in Large Time Series. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2002. Lecture Notes in Computer Science, vol 2431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45681-3_5
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DOI: https://doi.org/10.1007/3-540-45681-3_5
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