Often time series are organized into a hierarchy. For example, the total visitors of a country can be divided into regions and the visitors of each region can be further divided into sub-regions. This is a hierarchical time series. Hierarchical forecasts should be coherent; for instance, the sum of the forecasts of the different regions should equal the forecast for the total. The forecasts are incoherent if they do not satisfy such constraints. Temporal hierarchies are another application of hierarchical time series, in which the same variable is predicted at different scales (e.g., monthly, quarterly and yearly) and coherence across the different temporal scales is needed. Reconciliation is the process of adjusting forecasts which are created independently for each time series, so that they become coherent. I will discuss the state-of-the-art of reconciliation algorithms.