Saved in:
Bibliographic Details
Main Authors: Knapik, Teodor, Ratiarison, Adolphe, Razafindralambo, Hasina
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.16507
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910381630291968
author Knapik, Teodor
Ratiarison, Adolphe
Razafindralambo, Hasina
author_facet Knapik, Teodor
Ratiarison, Adolphe
Razafindralambo, Hasina
contents Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks.
format Preprint
id arxiv_https___arxiv_org_abs_2403_16507
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An experimental evaluation of choices of SSA forecasting parameters
Knapik, Teodor
Ratiarison, Adolphe
Razafindralambo, Hasina
Computational Engineering, Finance, and Science
Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks.
title An experimental evaluation of choices of SSA forecasting parameters
topic Computational Engineering, Finance, and Science
url https://arxiv.org/abs/2403.16507