Trend analysis in L-fuzzy contexts with absent values

Sometimes we have to work with  $L$-fuzzy context sequences where one or more values are missing. These sequences can represent, among other things, the evolution in time of an $L$-fuzzy context. The studies of tendencies that we have done so far used tools that are not valid when the $L$-fuzzy context has unknown values. In this work we address such situations and we propose new methods to tackle the problem. Besides, we use the study of tendencies to analyse relations between the objects and the attributes of $L$-fuzzy contexts and to replace the absent values  taking into account the behaviour of the sequence.