Functional time series forecasting: Functional singular spectrum analysis approaches

Author:

Trinka Jordan1ORCID,Haghbin Hossein2ORCID,Lin Shang Han3ORCID,Maadooliat Mehdi1ORCID

Affiliation:

1. Department of Mathematical and Statistical Sciences Marquette University Milwaukee Wisconsin USA

2. Department of Intelligent Systems and Data Science Persian Gulf University Bushehr Bushehr Province Iran

3. Department of Actuarial Studies and Business Analytics Macquarie University Sydney New South Wales Australia

Abstract

AbstractWe introduce two novel nonparametric forecasting methods designed for functional time series (FTS), namely, functional singular spectrum analysis (FSSA) recurrent and vector forecasting. Our algorithms rely on extracted signals obtained from the FSSA method and innovative recurrence relations to make predictions. These techniques are model‐free, capable of predicting nonstationary FTS and utilize a computational approach for parameter selection. We also employ a bootstrap algorithm to assess the goodness‐of‐prediction. Through comprehensive evaluations on both simulated and real‐world climate data, we showcase the effectiveness of our techniques compared to various parametric and nonparametric approaches for forecasting nonstationary stochastic processes. Furthermore, we have implemented these methods in the Rfssa R package and developed a shiny web application for interactive exploration of the results.

Funder

National Science Foundation

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

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