Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length. •Detection of signals in pharmacovigilance datasets is improved by temporal dynamics.•TEDAR detects a greater number of true signals without increasing false positives.•TEDAR detects adverse reactions months before the other methodologies.