The inevitable success of predictive analytics lies in the prospect of cutting edge approaches proposed in the domain of data mining and machine learning. Frequent itemsets, sequential pattern mining has been the major sub domains of the data mining since its inception with human work. Sequence prediction is a rather more targeted approach towards the predictive data mining. Sequential pattern mining approaches, while being very efficient in finding associations and interactions of difference itemsets and discovering hidden pattern in the transactional or graph datasets, sequence prediction is better focused on predicting the next item of an existent sequence. Applications of sequence prediction has been in pattern prediction, stock value prediction, websites prefetching, fraud detection, breach prediction, text prediction, product recommendation in extensive manner. Several state-ofthe-art algorithms and models have been proposed starting from graphical models, markovian models and tree based structures have been proposed by data mining. Machine learning models like recurrent neural networks with LSTM blocks work pretty well in terms of performance. Our aim is to propose an integrated data structure with better performance measures (space-time efficiency, scalability) and which can be effectively used in online learning setting-stream processing algorithm. The paper also describes the extension prospect of this algorithm which can be used in the time-series forecast