Functional methods for time series prediction: a nonparametric approach
From MaRDI portal
Publication:3018664
DOI10.1002/for.1169zbMath1217.91138OpenAlexW2059977044WikidataQ61849267 ScholiaQ61849267MaRDI QIDQ3018664
Germán Aneiros-Pérez, Ricardo Cao, Juan M. Vilar Fernández
Publication date: 27 July 2011
Published in: Journal of Forecasting (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/for.1169
Related Items
Choosing the most relevant level sets for depicting a sample of densities ⋮ A functional linear model for time series prediction with exogenous variables ⋮ Sieve bootstrap for functional time series ⋮ Improvement of the nonparametric estimation of functional stationary time series using Yeo-Johnson transformation with application to temperature curves ⋮ Locally modelled regression and functional data ⋮ On projection methods for functional time series forecasting
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Local smoothing regression with functional data
- Time series: theory and methods
- Nonparametric curve estimation from time series
- Nonparametric statistics for stochastic processes. Estimation and prediction.
- Nonparametric vector autoregression
- An overview of bootstrap methods for estimating and predicting in time series
- Linear processes in function spaces. Theory and applications
- Nonparametric time series prediction: A semi-functional partial linear modeling
- Nonparametric regression estimation for dependent functional data: asymptotic normality
- Nonparametric regression for functional data: automatic smoothing parameter selection
- Autoregressive Forecasting of Some Functional Climatic Variations
- Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality
- A Functional Wavelet–Kernel Approach for Time Series Prediction
- Nonparametric Forecasting in Time Series - A Comparative Study
- Bootstrap Prediction Intervals for Autoregression
- KERNEL REGRESSION SMOOTHING OF TIME SERIES
- Nonparametric forecasting: a comparison of three kernel-based methods
- Non‐parametric vs parametric forecasting in time series: A computational point of view
- Nonparametric Identification of Nonlinear Time Series: Selecting Significant Lags
- IMPROVED BOOTSTRAP PREDICTION INTERVALS FOR AUTOREGRESSIONS
- Some automated methods of smoothing time-dependent data
- A Review of Nonparametric Time Series Analysis
- Predicting Using Box-Jenkins, Nonparametric, and Bootstrap Techniques
- Local linear regression for estimating time series data.
