Incrementally Optimized Very Fast Decision Tree (iOVFDT) is a new data stream mining model that optimizes a balance of compact tree size and prediction accuracy. The iOVFDT was developed into open source on Massive Online Analysis as a prior art. In this book chapter, we review related techniques and extend iOVFDT into iOVFDF ('F' for forest of Trees) for temporal data stream mining. A framework for follow-up research is reported in this article. A major issue to the current temporal data mining algorithms is due to the inherent limitation of batch learning. But in real-life, the hidden concepts of data streams may change rapidly, and the data may amount to infinity. In the big Data era, incremental learning is attractive since it does not require processing the full volume of dataset. Under this framework we propose to research and develop a new breed of temporal data stream algorithms-iOVFDF. We integrate for a "meta-classifier" called iOVFD Forest over a collection of iOVFDT classifiers. The new iOVFD Forest can incrementally learn temporal associations across multiple time-series in real-time, while each underlying individual iOVFDTree learns and recognizes sub-sequence patterns dynamically.