In further detail, the Theta method (manipulation of local curvatures), Multiple Temporal Aggregation (MTA), bootstrapping, Forecasting with Sub-seasonal Series (FOSS) and forecasting with multiple starting points are discussed and compared in terms of how information is extracted from data, the computational cost and the performance. [...]the concept of the “wisdom of the data” is presented, explaining how a proper data manipulation can translate into improved forecast accuracy by combining forecasts carried out from different perspectives on the same data. [...]an unsupervised clustering based on k-means algorithm applied to all possible PV+BESS (Battery Energy Storage System) configurations allowed the researchers to identify the family of feasible solutions which, as expected, was characterized by a low payback time and a low number of residual cycles. According to the authors, the developed model can even be generalized to different types of consumption, such as electricity and gas. The proposed method is tested on estimating the Loop Current (LC) measured in the Gulf of Mexico between 2009 and 2011 at multiple spatial and temporal scales, where an RMSE (Root Mean Square Error) lower than 0.05 cm/s and a correlation coefficient of 0.6 were presented. [...]the model presented a useful forecast period, hence the time interval after which the forecast significantly diverges from the observed motion field, larger than 4 days.
Editorial for Special Issue: “Feature Papers of Forecasting 2021”