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).
Interval predictor models: identification and reliability
GARATTI, SIMONE
2009-01-01
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).File | Dimensione | Formato | |
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Automatica_Campi_Calafiore_Garatti_2009.pdf
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