Homogenising time series: beliefs, dogmas and facts
In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with highe...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2011-06-01
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| Series: | Advances in Science and Research |
| Online Access: | http://www.adv-sci-res.net/6/167/2011/asr-6-167-2011.pdf |
| _version_ | 1811343964289105920 |
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| author | P. Domonkos |
| author_facet | P. Domonkos |
| author_sort | P. Domonkos |
| collection | DOAJ |
| description | In the recent decades various homogenisation methods have been developed,
but the real effects of their application on time series are still not known
sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to
reveal the real impacts of homogenisation methods more detailed and with
higher confidence than earlier. As a part of the COST activity, a benchmark
dataset was built whose characteristics approach well the characteristics of
real networks of observed time series. This dataset offers much better
opportunity than ever before to test the wide variety of homogenisation
methods, and analyse the real effects of selected theoretical
recommendations.
<br><br>
Empirical results show that real observed time series usually include
several inhomogeneities of different sizes. Small inhomogeneities often have
similar statistical characteristics than natural changes caused by climatic
variability, thus the pure application of the classic theory that
change-points of observed time series can be found and corrected one-by-one
is impossible. However, after homogenisation the linear trends, seasonal
changes and long-term fluctuations of time series are usually much closer to
the reality than in raw time series. Some problems around detecting multiple
structures of inhomogeneities, as well as that of time series comparisons
within homogenisation procedures are discussed briefly in the study. |
| first_indexed | 2024-04-13T19:39:20Z |
| format | Article |
| id | doaj.art-e2cfccc6a17440ce8ad33c872d48cc32 |
| institution | Directory Open Access Journal |
| issn | 1992-0628 1992-0636 |
| language | English |
| last_indexed | 2024-04-13T19:39:20Z |
| publishDate | 2011-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Science and Research |
| spelling | doaj.art-e2cfccc6a17440ce8ad33c872d48cc322022-12-22T02:32:56ZengCopernicus PublicationsAdvances in Science and Research1992-06281992-06362011-06-01616717210.5194/asr-6-167-2011Homogenising time series: beliefs, dogmas and factsP. Domonkos0Centre for Climate Change (C3), Geography Dept., University Rovira i Virgili, Campus Terres de l'Ebre, C. Betánia 5, Tortosa, 43500, SpainIn the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. <br><br> Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.http://www.adv-sci-res.net/6/167/2011/asr-6-167-2011.pdf |
| spellingShingle | P. Domonkos Homogenising time series: beliefs, dogmas and facts Advances in Science and Research |
| title | Homogenising time series: beliefs, dogmas and facts |
| title_full | Homogenising time series: beliefs, dogmas and facts |
| title_fullStr | Homogenising time series: beliefs, dogmas and facts |
| title_full_unstemmed | Homogenising time series: beliefs, dogmas and facts |
| title_short | Homogenising time series: beliefs, dogmas and facts |
| title_sort | homogenising time series beliefs dogmas and facts |
| url | http://www.adv-sci-res.net/6/167/2011/asr-6-167-2011.pdf |
| work_keys_str_mv | AT pdomonkos homogenisingtimeseriesbeliefsdogmasandfacts |