Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4946968 | Neurocomputing | 2017 | 31 Pages |
Abstract
Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings of distributions, in this paper we apply this methodology for the kernel embedding of an autoregressive process of order p. By doing so, we provide a non-linear version of an autoregressive process, that shows increased performance over the linear model in highly complex time series. We use the method proposed for one-step ahead forecasting of different time-series, and compare its performance against other non-linear methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Edgar A. Valencia, Mauricio A. Álvarez,