This report consists of a brief summary of the “boom and bust” tool developed by Parma University inPrimeFish and twopeer reviewed papersabout the MATLAB implementation of the method in the FSDA toolbox developed by the University of Parma. A third paper is under revision. The intended audience of the deliverable and the journalarticle is primarily the scientific community, but the executive summary is written in plain language and is therefore understandable to other stakeholder groups.
Based on work carried out in task 2.3 of PrimeFish “Identifying and characterising “boom and bust cycles” and D2.4, the Flexible Statistics and Data Analysis (FSDA) toolbox wasused to predict price behaviour in the growth risk analyser (GRA) of PrimeDSS. To simplify the calculations carried out inGRA, a simple statistical modelbased on Robust Monitoring of Time Series approachwas used. The Kalman filter used there generated 4-5 graphs for each analysis that had to be read together. As only statisticians could probably fully grasp the information revealed in the graphs, it was deemed necessary to develop more easily accessible presentations. For this, new models were compiled for use in WP5 and WP6 that summarisethe price forecast in a single chart joined by a table that shows the price expected for each month and the extreme prices that can occur.