Optimal Short-Term Forecast for Locally Stationary Functional Time Series
Methodology
2023-07-19 v1
Abstract
Accurate curve forecasting is of vital importance for policy planning, decision making and resource allocation in many engineering and industrial applications. In this paper we establish a theoretical foundation for the optimal short-term linear prediction of non-stationary functional or curve time series with smoothly time-varying data generating mechanisms. The core of this work is to establish a unified functional auto-regressive approximation result for a general class of locally stationary functional time series. A double sieve expansion method is proposed and theoretically verified for the asymptotic optimal forecasting. A telecommunication traffic data set is used to illustrate the usefulness of the proposed theory and methodology.
Keywords
Cite
@article{arxiv.2307.09148,
title = {Optimal Short-Term Forecast for Locally Stationary Functional Time Series},
author = {Yan Cui and Zhou Zhou},
journal= {arXiv preprint arXiv:2307.09148},
year = {2023}
}