2013/02/14 by Jairo Cugliari, Cugliari, Jairo
Computer Science · Engineering · Mathematics · #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Neural Networks and Applications #stat.ME
paper · pdf · doi:10.48550/arxiv.1302.3488
arxiv created 2013/02/14 · openalex publication_date 2013/02/14 · arxiv updated 2013/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When considering the problem of forecasting a continuous-time stochastic process over an entire time-interval in terms of its recent past, the notion of Autoregressive Hilbert space processes (ARH) arises. This model can be seen as a generalization of the classical autoregressive processes to Hilbert space valued random variables. Its estimation presents several challenges that were addressed by many authors in recent years. In this paper, we propose an extension based on this model by introducing a conditioning process on the arh. In this way, we are aiming a double objective. First, the intrinsic linearity of arh is overwhelm. Second, we allow the introduction of exogenous covariates on this function- valued time series model. We begin defining a new kind of processes that we call Conditional arh. We then propose estimators for the infinite dimensional parameters associated to such processes. Using two classes of predictors defined within the arh framework, we extend these to our case. Consistency results are provided as well as a real data application related to electricity load forecasting.