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Time-series benchmarks based on frequency features for fair comparative evaluation
Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of freq...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122570/ https://www.ncbi.nlm.nih.gov/pubmed/37362566 http://dx.doi.org/10.1007/s00521-023-08562-5 |
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author | Wu, Zhou Jiang, Ruiqi |
author_facet | Wu, Zhou Jiang, Ruiqi |
author_sort | Wu, Zhou |
collection | PubMed |
description | Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset. |
format | Online Article Text |
id | pubmed-10122570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-101225702023-04-24 Time-series benchmarks based on frequency features for fair comparative evaluation Wu, Zhou Jiang, Ruiqi Neural Comput Appl Original Article Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset. Springer London 2023-04-22 /pmc/articles/PMC10122570/ /pubmed/37362566 http://dx.doi.org/10.1007/s00521-023-08562-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Wu, Zhou Jiang, Ruiqi Time-series benchmarks based on frequency features for fair comparative evaluation |
title | Time-series benchmarks based on frequency features for fair comparative evaluation |
title_full | Time-series benchmarks based on frequency features for fair comparative evaluation |
title_fullStr | Time-series benchmarks based on frequency features for fair comparative evaluation |
title_full_unstemmed | Time-series benchmarks based on frequency features for fair comparative evaluation |
title_short | Time-series benchmarks based on frequency features for fair comparative evaluation |
title_sort | time-series benchmarks based on frequency features for fair comparative evaluation |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122570/ https://www.ncbi.nlm.nih.gov/pubmed/37362566 http://dx.doi.org/10.1007/s00521-023-08562-5 |
work_keys_str_mv | AT wuzhou timeseriesbenchmarksbasedonfrequencyfeaturesforfaircomparativeevaluation AT jiangruiqi timeseriesbenchmarksbasedonfrequencyfeaturesforfaircomparativeevaluation |