Statistics and Its Interface

Volume 16 (2023)

Number 1

Special issue on recent developments in complex time series analysis – Part I

Guest editors: Robert T. Krafty (Emory Univ.), Guodong Li (Univ. of Hong Kong), Anatoly Zhigljavsky (Cardiff Univ.)

Study of automatic choice of parameters for forecasting in singular spectrum analysis

Pages: 109 – 116

DOI: https://dx.doi.org/10.4310/21-SII707

Authors

Safia Al Marhoobi (Ministry of Higher Education, Research and Innovation, Oman; and School of Mathematics, Cardiff University, Cardiff, Wales, United Kingdom)

Andrey Pepelyshev (School of Mathematics, Cardiff University, Cardiff, Wales, United Kingdom)

Abstract

Singular spectrum analysis (SSA) is a popular tool for analysing and forecasting time series. The SSA forecasting algorithms have two parameters which should be chosen by the researcher or using the so-called automatic choice based on the root mean squared errors (RMSE) of retrospective forecasts. We study the sensitivity of the RMSE and investigate the reliability of the automatic choice of parameters for forecasting monthly temperature and humidity recorded at three meteorological stations in Oman.

Keywords

recurrent SSA, vector SSA, temperature, humidity

2010 Mathematics Subject Classification

62M20

Full Text (PDF format)

The work of A. Pepelyshev was partially supported by the Russian Foundation for Basic Research (project no. 20-01-00096).

The authors declare that they have no conflict of interest.

Received 28 April 2021

Accepted 1 September 2021

Published 28 December 2022