Improvement of the nonparametric estimation of functional stationary time series using Yeo-Johnson transformation with application to temperature curves

DOI10.1155/2021/6676400zbMath1478.62266OpenAlexW3127951730MaRDI QIDQ2247643

Sameera Abdulsalam Othman, Haithem Taha Mohammed Ali

Publication date: 17 November 2021

Published in: Advances in Mathematical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2021/6676400

zbMATH Keywords

functional stationary time series

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Meteorology and atmospheric physics (86A10)

Uses Software

• R

Cites Work

• Functional-coefficient models for nonstationary time series data
• Transformations and Bayesian density estimation
• Kernel density estimation of actuarial loss functions
• Properties of nonlinear transformations of fractionally integrated processes.
• Nonparametric functional data analysis. Theory and practice.
• A new family of power transformations to improve normality or symmetry
• Functional methods for time series prediction: a nonparametric approach
• Nonparametric Forecasting in Time Series - A Comparative Study
• Transformations in Density Estimation
• Forecasting intraday S&P 500 index returns: A functional time series approach
• On the Prediction of Stationary Functional Time Series
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Improvement of the nonparametric estimation of functional stationary time series using Yeo-Johnson transformation with application to temperature curves