Periodic Pattern mining in time series database

Periodic Pattern mining in time series database using Suffix tree

In our daily life activities, we can find several events/items that are repeated with respect of time; i.e. traffic signal, weekly TV program, selling items in storehouse. In business fields, we have to need to query about these type of products.

As a result, there are many approaches to find repeated patterns in database that is mostly known as periodic pattern in Data mining field.



In the data mining field, finding periodic patterns is an important feature that is used in enormous real life applications.


What is periodic pattern?

Example:

There are three types of periodic pattern.
1. Symbol periodicity,
2. Sequence periodicity,
3. Segment periodicity.


There are several ways to mine periodic patterns. Some existing algorithms find only Symbol periodicity, others find only sequence periodicity. However, Faraz rasheed et al. have proposed an algorithm that mine all types of periodic patterns in a single run.


Pseudo-code: (Mining Periodic Pattern)

Fig: Mining Periodic Pattern

Code:








Periodic Pattern mining in time series database

Periodic Pattern mining in time series database



References:

[1] F. Rasheed and R. Alhajj, “STNR: A Suffix Tree Based Noise Resilient Algorithm for Periodicity Detection in Time Series Databases,” Applied Intelligence, vol. 32, no. 3, pp. 267-278, 2010.

[2] F. Rasheed and R. Alhajj, “Using Suffix Trees for Periodicity Detection in Time Series Databases,” Proc. IEEE Int’l Conf. Intelligent Systems, Sept. 2008.

[3] M.G. Elfeky, W.G. Aref, and A.K. Elmagarmid, “Periodicity Detection in Time Series Databases,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 7, pp. 875-887, July 2005.

[4] M.G. Elfeky, W.G. Aref, and A.K. Elmagarmid, “WARP: Time Warping for Periodicity Detection,” Proc. Fifth IEEE Int’l Conf. Data Mining, Nov. 2005.