Under specific environmental conditions, co-regulated genes and/or genes with similar functions tend to have similar temporal expression profiles. Identifying groups of genes with similar temporal profiles can therefore bring new insight into understanding of gene regulation and function. The most common way of discovering such groups of genes is with short time series clustering techniques. Once we have the clusters, we can also try to describe them in terms of some common characteristics of the comprising genes. An alternative way are the so-called constrained clustering techniques; here only clusters with valid descriptions are considered, and as a result, we obtain clusters and their descriptions in one single step. We present a novel constrained clustering method for short time series, which uses the approach of predictive clustering. Predictive clustering combines clustering and predictive modeling; it partitions the instances in a set of clusters like the regular clustering does, however, it also constructs predictive model(s) that describes each of the clusters. So far, predictive models can take the form of decision trees or rules. Predictive clustering trees, together with a qualitative time series distance measure, have already been used for clustering of short time series. Here we present predictive clustering rules for short time series, which use the same qualitative distance measure, but describe clusters with decision rules instead of trees.