Articolo in rivista, 2016, ENG, 10.1088/1748-0221/11/07/C07013

On determining the prediction limits of mathematical models for time series

Peluso E.; Murari A.; Gelfusa M.; Lungaroni M.; Talebzadeh S.; Gaudio P.

1,3,4,5,6: Department of Industrial Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, Rome, 00133, Italy; 2: Consorzio RFX, CNR, ENEA, INFN, Universita' di Padova, Acciaierie Venete SpA, Corso Stati Uniti 4, Padova, 35127, Italy.

Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

Journal of instrumentation 11 (7)

Keywords

Analysis and statistical methods, Calibration and fitting methods, Cluster finding, Data processing methods, Pattern recognition

CNR authors

Murari Andrea

CNR institutes

IGI – Istituto gas ionizzati