Choosing a Linear Model with a Random Number of Change-Points and Outliers

Caussinus, Henri; Lyazrhi, Faouzi

doi:10.1023/a:1003230713770

Cited by 67 publications

(58 citation statements)

References 30 publications

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“…However, the maximum external variance V max grows with each additionally assumed break, reaching full variance if n− 1 breaks were tested within a time series of length n. Such a useless continuation of inserting more and more test breaks is prevented by the stop criterion, which limits the numbers of breaks by a penalty term for each additional break. Following Caussinus and Lyazrhi (1997), we use…”

Section: The Break Search Methods Usedmentioning

confidence: 99%

The joint influence of break and noise variance on the break detection capability in time series homogenization

Lindau

Venema

2018

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. Instrumental climate records of the last centuries suffer from multiple breaks due to relocations and changes in measurement techniques. These breaks are detected by relative homogenization algorithms using the difference time series between a candidate and a reference. Modern multiple changepoint methods use a decomposition approach where the segmentation explaining most variance defines the breakpoints, while a stop criterion restricts the number of breaks. In this study a pairwise multiple breakpoint algorithm consisting of these two components is tested with simulated data for a range of signal-to-noise ratios (SNRs) found in monthly temperature station datasets. The results for low SNRs obtained by this algorithm do not differ much from random segmentations; simply increasing the stop criterion to reduce the number of breaks is shown to not be helpful. This can be understood by considering that, in case of multiple breakpoints, even a random segmentation can explain about half of the break variance. We derive analytical equations for the explained noise and break variance for random and optimal segmentations. From these we conclude that reliable break detection at low but realistic SNRs needs a new approach. The problem is relevant because the uncertainty of the trends of individual stations is shown to be climatologically significant also for these small SNRs. An important side result is a new method to determine the break variance and the number of breaks in a difference time series by studying the explained variance for random break positions. We further discuss the changes from monthly to annual scale which increase the SNR by more than a factor of 3.

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Section: The Break Search Methods Usedmentioning

confidence: 99%

The joint influence of break and noise variance on the break detection capability in time series homogenization

Lindau

Venema

2018

Adv. Stat. Clim. Meteorol. Oceanogr.

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“…In the ANOVA the observed values are considered to be a composition of climate-effect, station-effect and noise, and an equation-system is built and solved for the case of zero noise. Caussinus and Mestre (2004) proved that the equation system of the ANOVA provides the best estimation of IH-sizes. In that paper the full description of the ANOVA is provided.…”

Section: Homogenisation-adjustment With Anovamentioning

confidence: 99%

“…In the ACMANT the core of the detection and adjustment methods is the same as in the PRODIGE (the method of Caussinus and Mestre, 2004). The PRODIGE has a strong mathematical background, and earlier studies (Domonkos, 2006), as well as the homogenisation of COST HOME benchmark dataset (hereafter: benchmark; Venema et al, 2010) iv.…”

Section: Main Properties Of Acmantmentioning

confidence: 99%

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Newest developments of ACMANT

2011

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Abstract. The seasonal cycle of radiation intensity often causes a marked seasonal cycle in the inhomogeneities (IHs) of observed temperature time series, since a substantial portion of them have direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method, the Adapted Caussinus -Mestre Algorithm for Networks of Temperature series (ACMANT) has recently been developed which treats appropriately the seasonal changes of IH-sizes in temperature time series. The performance of ACMANT was proved to be among the best methods (together with PRODIGE and MASH) in the efficiency test procedure of COST ES0601 project. A further improved version of the ACMANT is described in this paper. In the new version the ANOVA procedure is applied for correcting inhomogeneities, and with this change the iterations applied in the earlier version have become unnecessary. Some other modifications have also been made, from which the most important one is the new way for estimating the timings of IHs. With these modifications the efficiency of the ACMANT has become even higher, therefore its use is strongly recommended when networks of monthly temperature series from mid-or high geographical latitudes are subjected to homogenisation. The paper presents the main properties and the operation of the new ACMANT.

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“…In the present application c 0 = 2 -0.5 . For selecting the most appropriate K', the CaussinusLyazrhi criterion [25] is applied (Equations (25) and (26)). …”

Section: Detection Processmentioning

confidence: 99%

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Domonkos¹

2011

IJG

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Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appears in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid-or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

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Choosing a Linear Model with a Random Number of Change-Points and Outliers

Cited by 67 publications

References 30 publications

The joint influence of break and noise variance on the break detection capability in time series homogenization

The joint influence of break and noise variance on the break detection capability in time series homogenization

Newest developments of ACMANT

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

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