Time series datasets consist of sequence of numeric values obtained over repeated measurements of time. They are Popular in many applications such as stock market analysis, power consumption, economic and sells forecasting, temperature etc. Periodic pattern mining or periodicity detection is process of finding periodic patterns in time series database. It has a number of applications, such as prediction, forecasting, detection of unusual activities, etc. Periodicity mining needs to give more attention as its increased need in real life applications. The types of periodicities are symbol periodicity, sequence periodicity and segment periodicity and they should be identified even in the presence of noise in the time series database. There are number of algorithms exists for periodic pattern mining. Those algorithms have some advantages and disadvantages. In this paper, we have compared different periodicity mining algorithms and given plan for developing efficient periodicity mining algorithm which detect symbol periodicity, sequence periodicity and segment periodicity and noise-resilient .