INFORMATION CHANGE THE WORLD

International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.4, No.10, Sep. 2012

Constraint Based Periodicity Mining in Time Series Databases

Full Text (PDF, 1271KB), PP.37-46


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Author(s)

Ramachandra.V.Pujeri,G.M.Karthik

Index Terms

Data Mining, CBPM, FP-tree, Periodicity mining, Time series data, Noise resilient

Abstract

The search for the periodicity in time-series database has a number of application, is an interesting data mining problem. In real world dataset are mostly noisy and rarely a perfect periodicity, this problem is not trivial. Periodicity is very common practice in time series mining algorithms, since it is more likely trying to discover periodicity signal with no time limit. We propose an algorithm uses FP-tree for finding symbol, partial and full periodicity in time series. We designed the algorithm complexity as O (kN), where N is the length of input sequence and k is length of periodic pattern. We have shown our algorithm is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both synthetic and real data from different domains have shown our algorithms' time efficient and noise-resilient feature. A comparison with some current algorithms demonstrates the applicability and effectiveness of the proposed algorithm.

Cite This Paper

Ramachandra.V.Pujeri,G.M.Karthik,"Constraint Based Periodicity Mining in Time Series Databases", IJCNIS, vol.4, no.10, pp.37-46, 2012.

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