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When a biological quantity examination exhibits a high degree of individuality, developing a strategy for interpreting these values in an individual context can be a useful alternative. Time-series analysis is the appropriate statistical framework to build a model for explanation of the behaviour of laboratory information and to forecast future values. The key concepts in this approach are autocorrelation and within-person variance. Unfortunately, the powerful tools provided by time-series analysis require many observations, a requisite difficult to meet in every day practice. However, introducing some restrictions in the autocorrelation parameter of the most reliable model, the first order autocorrelation model, and using the average within-person variance from a selected population, it is possible to build predictive reference intervals for an individual, based on only few observations. The most common case is the minimum time series: when there are just two observations. The statistical significance of the change from a previous observation is a problem that arises from both quality control (delta checks) and the interpretative diagnostic fields (reference change limit). Applying the same restrictive criteria, it is possible to develop specific limits for a difference between consecutive observations based on a within-person variance selected from the distribution of variances found in a sample of similar individuals.