Abstract: A time series is collection of well-defined data sets obtained through repeated measurements of time. Extraction of periodic pattern in a time series database is significant one in data mining problem that predicts and forecasts the future behavior of the data at regular time interval. Periodic pattern mining involves several applications such as prediction, forecasting, detection of unusual activities. The difficulty is not trivial because the data to be examined are regularly noisy and diverse periodicity types (that is symbol, sequence and segment) are to be examined. The whole time series or in a subsection of it to effectively handle various types of noise (to a definite degree) and at the same time to detect different types of periodic patterns. The existing suffix tree based periodic pattern mining algorithm can detect symbol, sequence and segment periodicity in time series data with noise filters for diverse noise kinds. But the running time desired to identify the patterns without redundancy is high. So, to overcome this issue, in this study, Predominant Pattern Distribution Model is introduced with which redundant and unwanted noisy patterns are identified and discarded from the time series data. Predominant patterns are extracted with automatic or user defined threshold of pattern of interest, generated from the dynamic online time series data. Experiments conducted on both synthetic and real data sets of research repositories including protein sequences. Performance of proposed framework is measured and evaluated in terms of periodic pattern mining accuracy, noise distribution rate and predominant pattern occurrence.