Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user's interest or preference. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. On-shelf utility mining considers on-shelf time period of item and gets the accurate utility values of itemsets in temporal database. In traditional on-shelf utility mining, profits of all items in databases are considered as positive values. However, in real applications, some items may have negative profit. Most of the traditional algorithms are used to handle static database. In practical situations, temporal databases are continually appended or updated. High on-shelf utility itemsets needs to be updated. Re-running the temporal mining algorithm every time is ineffective since it neglects previously discovered itemsets. It repeats the work done previously. In this paper, an effective algorithm is proposed to find high on-shelf utility itemsets with negative values from the dynamic updated temporal database.