Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach

2020/08/25 by Carré, Cl\'{e]ment, André Mas, Mas, André
Computer Science · #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2008.11155

openalex publication_date 2020/08/25 · openalex created_date 2022/09/16 · openalex updated_date 2026/07/28

Abstract

The autoregressive Hilbertian model (ARH) was introduced in the early 90's by Denis Bosq. It was the subject of a vast literature and gave birth to numerous extensions. The model generalizes the classical multidimensional autoregressive model, widely used in Time Series Analysis. It was successfully applied in numerous fields such as finance, industry, biology. We propose here to compare the classical prediction methodology based on the estimation of the autocorrelation operator with a neural network learning approach. The latter is based on a popular version of Recurrent Neural Networks : the Long Short Term Memory networks. The comparison is carried out through simulations and real datasets.

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