High utility pattern mining (HUPM) is one of the key areas in data mining, which is concerned with identifying patterns with high utility from transactional databases. The temporal factors such as periodicity and recency along with dynamic variations in profits have recently been added to pattern mining. However, no methods so far unify these dimensions in a common framework. To this end, in this paper we propose the DTU-Miner algorithm that integrates temporal constraints and dynamic profit updates to overcome such limitations. Through the use of advanced data structures such as UPR-List and P-set and the introduction of some novel pruning strategies, DTU-Miner surpasses state of the art in terms of Runtime, Memory and pattern quality. Results on benchmark datasets show that DTU-Miner outperforms state-of-the-art algorithms, CPR-Miner and iEFIM-Closed, which suggests the effectiveness of DTU-Miner over dense and sparse datasets including dynamic attributes.