Non‐parametric vs parametric forecasting in time series: A computational point of view

DOI10.1002/asm.3150090303zbMath0800.62563OpenAlexW2035598985MaRDI QIDQ4299524

Michel Carbon, Michel Delecroix

Publication date: 8 September 1994

Published in: Applied Stochastic Models and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asm.3150090303

zbMATH Keywords

kernel methodnon-parametric forecastingnon-parametric regression estimation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07)

Related Items (5)

Nonparametric estimation of a regression function and its derivatives under an ergodic hypothesis ⋮ Functional methods for time series prediction: a nonparametric approach ⋮ On a partly linear autoregressive model with moving average errors ⋮ Nonparametric forecasting: a comparison of three kernel-based methods ⋮ Vector-on-function quantile regression for stationary ergodic processes

This page was built for publication: Non‐parametric vs parametric forecasting in time series: A computational point of view