Efficient Method for De-Duplication and Periodicity Mining in Time Series Databases
Periodic pattern mining or periodicity detection has a number of applications, such as prediction, forecasting, detection of unusual activities, etc. The problem is not trivial because the data to be analyzed are mostly noisy and different periodicity types (namely symbol, sequence, and segment) are to be investigated. Noise is the duplication of data from different databases when they are used for same purpose in different places. So it should be removed. Time series is a collection of data values gathered generally at uniform interval of time to reflect certain behavior of an entity. Real life has several examples of time series such as weather conditions of a particular location, transactions in a superstore, network delays, power consumption, earthquake prediction.