A new method for abrupt change detection in dynamic structures

He, Wenping; Feng, Guolin; Wu, Qiong; Wan, Shiquan; Jifan, Chou

doi:10.5194/npg-15-601-2008

Cited by 54 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the reference (He et al, 2008), in order to quantitatively identify the time-instants of abrupt changes, the variances of scaling exponents are analysed based on the MC-DFA results. Thus, the effects of cutting data on the calculation of scaling exponents can be estimated.…”

Section: Abrupt Dynamic Change Detection In Correlated Time Seriesmentioning

confidence: 99%

“…The detection results of traditional methods for detecting abrupt change, such as Moving t-test, Cramer method, Mann-Kendall test (Mann, 1945;Kendall and Charles, 1975) and Yamamoto method (Yamamoto et al, 1985), all highly depend on the length of subseries, namely analysed timescales (He et al, 2008). However, the timeinstant of abrupt dynamic change does not have certain relations with specific timescales.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, traditional methods cannot effectively detect abrupt dynamic changes. To deal with this problem, moving detrended fluctuation analysis (MDFA) was proposed (He et al, 2008). The tests on different model time series indicate that MDFA can be used to effectively detect abrupt dynamic changes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new method for abrupt dynamic change detection of correlated time series

He¹,

Feng²,

Wu³

et al. 2011

Intl Journal of Climatology

View full text Add to dashboard Cite

On the basis of detrended fluctuation analysis (DFA), a new method, moving cut data-DFA (MC-DFA), was presented to detect abrupt dynamic change in correlated time series. The numerical tests show the capability of the presented method to detect abrupt change time-instants in model time series generated by Logistic map. Moving DFA (MDFA) and approximate entropy (ApEn) can provide some information such as a single time-instant of abrupt dynamic change, but both of them cannot exactly detect all of the abrupt change regions. Some traditional methods, such as moving t-test, Cramer method, Mann-Kendall test and Yamamoto method, even cannot provide any information of abrupt dynamic change in these model time series. Meanwhile, results showed that windows sizes and strong noise have some less effect on the MC-DFA results. In summary, MC-DFA provides a reliable measure to detect the abrupt dynamic change in correlated time series, and perfectively make up the deficiencies of MDFA and ApEn. The applications in daily surface air pressure records further verify the validity of the present method.

show abstract

Section: Abrupt Dynamic Change Detection In Correlated Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new method for abrupt dynamic change detection of correlated time series

He¹,

Feng²,

Wu³

et al. 2011

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…Scientists have long been aware that time series often cannot be described adequately by a single linear trend, since discontinuities may arise [4,5]. Recently there has been a boom in papers on both detecting the changepoints and revealing the phenomena behind them [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, numerous studies exist dealing with climate conditions -regardless whether the subjects of the analysis are instrumental [28] or proxy data -where, e.g. abrupt changes are sought after [7,8,10,29]. The fact that BPs can be triggered by non-climate factors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

et al. 2016

View full text Add to dashboard Cite

Time series often contain breakpoints of di erent origin, i.e. breakpoints, caused by (i) shifts in trend, (ii) other changes in trend and/or, (iii) changes in variance. In the present study, arti cially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. The time series are modi ed so that the exact "locations" of the arti cial breakpoints are prescribed, making it possible to evaluate the methods exactly. Hence, the study provides a deeper insight into the behaviour of the three di erent breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in reallife data sets, as is demonstrated with two examples taken from the elds of paleoclimate research and petrology.

show abstract

Evaluation of the performance of the Beijing Climate Centre Climate System Model 1.1(m) to simulate precipitation across China based on long‐range correlation characteristics

Zhao

2015

JGR Atmospheres

View full text Add to dashboard Cite

In this study, we investigated the observed daily precipitation data in China using detrended fluctuation analysis. The daily precipitation records exhibit relatively weak long‐range correlation (LRC) characteristics with scaling exponents of less than 0.6 in most of China. The performance of the climate model can be quantitatively evaluated by comparing the differences of the scaling exponents between the observational data and the model‐simulated data. Based on these findings, the performance of the Beijing Climate Centre Climate System Model (BCC_CSM1.1(m)) was evaluated for simulations of daily precipitation in China. The LRCs of the model‐simulated daily precipitation were stronger than those of the observations in the midwest of Northwest China and in the majority of North China but were weaker in the middle and eastern parts of Inner Mongolia, Jilin, and the area between the Yangtze River and the Huaihe River. In general, the average scaling exponents of the model‐simulated daily precipitation were greater than those of the observational data in all four seasons; the minimum of the simulated error occurred in winter, and the maximum occurred in autumn. Moreover, obvious differences were observed between the coefficients of the skewness and the kurtosis of the scaling exponents in the simulations and the observations.

show abstract

A new method for abrupt change detection in dynamic structures

Cited by 54 publications

References 27 publications

A new method for abrupt dynamic change detection of correlated time series

A new method for abrupt dynamic change detection of correlated time series

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

Evaluation of the performance of the Beijing Climate Centre Climate System Model 1.1(m) to simulate precipitation across China based on long‐range correlation characteristics

Contact Info

Product

Resources

About