Estimating functional time series by moving average model fitting

TL;DR

This paper introduces a novel method for estimating invertible functional time series by fitting functional moving average models using principal component projections, supported by asymptotic theory and empirical validation.

Contribution

It proposes a principled approach to estimate functional MA processes via principal directions, including strategies for model order selection and consistency proof.

Findings

Method performs well in simulations

Effective in modeling vehicle traffic data

Provides consistent estimators with asymptotic guarantees

Abstract

Functional time series have become an integral part of both functional data and time series analysis. Important contributions to methodology, theory and application for the prediction of future trajectories and the estimation of functional time series parameters have been made in the recent past. This paper continues this line of research by proposing a first principled approach to estimate invertible functional time series by fitting functional moving average processes. The idea is to estimate the coefficient operators in a functional linear filter. To do this a functional Innovations Algorithm is utilized as a starting point to estimate the corresponding moving average operators via suitable projections into principal directions. In order to establish consistency of the proposed estimators, asymptotic theory is developed for increasing subspaces of these principal directions. For practical purposes, several strategies to select the number of principal directions to include in the estimation procedure as well as the choice of order of the functional moving average process are discussed. Their empirical performance is evaluated through simulations and an application to vehicle traffic data.