On the trend, detrending, and variability of nonlinear and nonstationary time series

By Zhaohua Wu, Norden E. Huang, Steven R. Long, Chung-Kang Peng. Proceedings of the National Academy of Sciences of the United States of America, 2007

913 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/17ca200df8123873d86f356f9cb28f0c56fba36b

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Citations in the corpus

The modulated annual cycle: an alternative reference frame for climate anomalies (2007, methodology): “…and temporally local time series analysis to analyze MAC, particularly, the empirical mode decomposition (Huang et al. 1998, 1999, 2003; Flandrin et al. 2004; Wu and Huang 2004, 2005a; Wu et al. 2007; Huang and Wu 2008) and its most recent improvement, the Ensemble EMD (Wu and Huang 2005b, 2008).”
Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach (2011, background): “Onoz and Bayazit, 2003; Wu et al., 2007; Jhajharia et al., 2009), it has been discussed that a non-parametric test for trend is more feasible for non-stationary time series than the parametric methods.”
Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature (2012, methodology): “EMD and EEMD have been shown to be able to extract nonlinear trends in climatic time series (e.g., Huang et al. 1998; Wu et al. 2007; Wu and Huang 2009; Wu et al. 2011; Franzke 2009, 2010; Franzke and Woollings 2011; Qian et al. 2011).”
Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “…has been demonstrated and this approach has been used to analyze various types of climate data (Xie et al., 2002; Duffy, 2005; Li and Davis, 2006; Wu et al., 2007; Pegram et al., 2008; McDonald et al., 2007; McMahon et al., 2008; Huang et al., 2009; Franzke, 2009; Lee and Ouarda, 2010, 2012;…”
Discrete wavelet‐based trend identification in hydrologic time series (2013, background): “Wu et al. (2007) defined trend as an intrinsically fitted monotonic function or a function in which there can be at most one extremum within a given data span.”
Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting (2015, methodology): “EEMD is a new noise assisted data analysis method proposed by Huang et al. (Wu et al. 2007; Huang and Wu 2008) to overcome the shortcoming of mode mixing caused by signal intermittency in EMD (Huang et al. 2009).”
The influence of anthropogenic aerosol on multi-decadal variations of historical global climate (2013, background): “The number of IMFs identiﬁed is typically ln N , where N is the number of data points (Wu et al 2007).”
Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition (2012, methodology): “This is because the trend component might be induced by the inability of the EMD procedure to efficiently capture the IMF components [Lee and Ouarda, 2010], and also because the current trend may be defined only for the time span of the observed data [Wu et al., 2007; Huang and Wu, 2008].”
Recent responses to climate change reveal the drivers of species extinction and survival (2020, methodology): “We used default parameters in the R function emd, which are optimized for detrending climatic time series (38, 39).”
Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App). (2015, methodology): “Each oscillation component, termed an intrinsic mode function (IMF), is sequentially decomposed from the original time series by a sifting process (Huang et al., 1998; Wu et al., 2007).”
Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014 (2017, background): “The trend should be removed before other basic applications are implemented, such as computing the correlation function (Wu et al., 2007).”
Bayesian forecasting of immigration to selected European countries by using expert knowledge (2010, background): “As noted by Wu et al. (2007), properly defined trends for non-stationary and non-linear series would have to be intrinsic tothedataandthustimevarying.”