Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach

• Published in: arXiv.org
• Main Authors: Cl\'{e]ment Carré, Mas, André
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 25.08.2020
• Subjects: Autoregressive models; Autoregressive processes; Computer simulation; Neural networks; Recurrent neural networks; Short term; Time series
• ISSN: 2331-8422

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.