Nowadays, many databases record ordered or temporally annotated data, such as Web access logs or genomic sequences. Therefore, sequence mining has become an important research area. Among these data mining approaches, sequential patterns aim at describing frequent behaviors. In the access data of a commercial Web site, one may, for instance, discover that "35% of customers successively buy a PSP then a memory stick and PSP games". To provide more complete information, fuzzy sequential patterns were designed, including quantitative values within the mining task. Such patterns, considering the previous example, would be "35% of customers buy a PSP, then they buy few games many times, and then they buy a high-capacity memory stick once." However, symbolic or fuzzy sequential patterns, in their current form, do not allow to extract temporal tendencies that are typical of sequential data. By means of temporal tendency mining, one may discover in the same access data that "An increasing number of purchases of PSP games during a very short period is frequently followed by a purchase of a high-capacity memory stick a few days later." It would be easy to conclude that the users either quickly succeed in registering or make several attempts before they look at the help page within a few seconds. To the best of our knowledge, no method has been designed for discovering this kind of patterns. Therefore, we propose, in this paper, two approaches that extract pattern-expressing trends or evolution. First, we define evolution patterns that summarize the evolution of the quantities in the data. We explain how they can be obtained from a quantitative sequence database. Second, we define gradual trends in fuzzy sequential data. These trends describe variations in the fulfillment of fuzzy properties according to time. For both kinds of patterns, we developed algorithms that were implemented and tested on real data.