The assessment of the interestingness of sequential rules (generally temporal rules) is a crucial problem in sequence analysis. Due to their unsupervised nature, frequent pattern mining algorithms commonly generate a huge number of rules. However, while association rule interestingness has been widely studied in the literature, there are few measures dedicated to sequential rules. In this article, we propose an original statistical measure for assessing sequential rule interestingness. This measure named Sequential Implication Intensity (SII) evaluates the statistical significance of the rules in comparison with a probabilistic model. Numerical simulations show that SII has unique features for a sequential rule interestingness measure.