Interval predictor models: Identification and reliability |
| |
Authors: | MC Campi [Author Vitae] G Calafiore [Author Vitae] S Garatti [Author Vitae] |
| |
Affiliation: | a Dipartimento di Elettronica per l’Automazione - Università di Brescia, via Branze 38, 25123 Brescia, Italy b Dipartimento di Automatica e Informatica - Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy c Dipartimento di Elettronica e Informatica - Politecnico di Milano, P.zza Leonardo da Vinci 32, 20133 Milano, Italy |
| |
Abstract: | This paper addresses the problem of constructing reliable interval predictors directly from observed data. Differently from standard predictor models, interval predictors return a prediction interval as opposed to a single prediction value. We show that, in a stationary and independent observations framework, the reliability of the model (that is, the probability that the future system output falls in the predicted interval) is guaranteed a priori by an explicit and non-asymptotic formula, with no further assumptions on the structure of the unknown mechanism that generates the data. This fact stems from a key result derived in this paper, which relates, at a fundamental level, the reliability of the model to its complexity and to the amount of available information (number of observed data). |
| |
Keywords: | Set-valued models Interval prediction Convex optimization Model identification Statistical learning |
本文献已被 ScienceDirect 等数据库收录! |
|