Testing for nonlinearity in European climatic time series by the method of surrogate data

Mikšovský, Jiří; Raidl, Aleš

doi:10.1007/s00704-005-0130-7

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…Because the data points in this period are not outliers but rather belong to another population, the linear trend models may not properly represent the variability in the data. In contrast, nonlinear methods (e.g., Miksovsky and Raidl 2006) may be the better choice to account for the variability and support the upward temperature trend in the late twentieth century.…”

Section: Discussionmentioning

confidence: 99%

Application and Comparison of Robust Linear Regression Methods for Trend Estimation

Muhlbauer

Spichtinger

Lohmann

2009

Journal of Applied Meteorology and Climatology

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In this study, robust parametric regression methods are applied to temperature and precipitation time series in Switzerland and the trend results are compared with trends from classical least squares (LS) regression and nonparametric approaches. It is found that in individual time series statistically outlying observations are present that influence the LS trend estimate severely. In some cases, these outlying observations lead to an over-/underestimation of the trends or even to a trend masking. In comparison with the classical LS method and standard nonparametric techniques, the use of robust methods yields more reliable trend estimations and outlier detection.

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Section: Discussionmentioning

confidence: 99%

Application and Comparison of Robust Linear Regression Methods for Trend Estimation

Muhlbauer

Spichtinger

Lohmann

2009

Journal of Applied Meteorology and Climatology

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“…But it also turns out that the information content in a single time series is not always sufficient for the climate system's state characterization. This is especially true when the nonlinear component of the analyzed signal is to be studied (Miksovsky and Raidl, 2005). Fortunately, meteorological measurements (or numerical model outputs, reanalyses and similar data sets) are typically available for several variables and in numerous locations, which allows for the use of multivariate phase space reconstruction (Keppenne and Nicolis, 1989).…”

Section: Choice Of Predictorsmentioning

confidence: 99%

“…Tang et al, 2000), despite the fact that the meteorological series originate from an inherently nonlinear system. Application of nonlinearity tests reveals some climatic data sets to appear linear (Palus and Novotna, 1994;Schreiber and Schmitz, 2000;Miksovsky and Raidl, 2005), while others may exhibit nonlinear characteristics (Palus and Novotna, 1994;Palus, 1996;Tsonis, 2001;Miksovsky and Raidl, 2005). In this paper, our intention was to address the problem of nonlinearity of the atmospheric time series from a rather practical point of view and to ascertain the performance of several nonlinear methods of time series analysis for two typical meteorological problems: construction of temporal (prediction) and spatial (downscaling) mappings at synoptic time scales.…”

Section: Introductionmentioning

confidence: 99%

Testing the performance of three nonlinear methods of time seriesanalysis for prediction and downscaling of European daily temperatures

Mikšovský

Raidl

2005

Nonlin. Processes Geophys.

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Abstract.We investigated the usability of the method of local linear models (LLM), multilayer perceptron neural network (MLP NN) and radial basis function neural network (RBF NN) for the construction of temporal and spatial transfer functions between different meteorological quantities, and compared the obtained results both mutually and to the results of multiple linear regression (MLR). The tested methods were applied for the short-term prediction of daily mean temperatures and for the downscaling of NCEP/NCAR reanalysis data, using series of daily mean, minimum and maximum temperatures from 25 European stations as predictands. None of the tested nonlinear methods was recognized to be distinctly superior to the others, but all nonlinear techniques proved to be better than linear regression in the majority of the cases. It is also discussed that the most frequently used nonlinear method, the MLP neural network, may not be the best choice for processing the climatic time series -LLM method or RBF NNs can offer a comparable or slightly better performance and they do not suffer from some of the practical disadvantages of MLPs.Aside from comparing the performance of different methods, we paid attention to geographical and seasonal variations of the results. The forecasting results showed that the nonlinear character of relations between climate variables is well apparent over most of Europe, in contrast to rather weak nonlinearity in the Mediterranean and North Africa. No clear large-scale geographical structure of nonlinearity was identified in the case of downscaling. Nonlinearity also seems to be noticeably stronger in winter than in summer in most locations, for both forecasting and downscaling.

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“…Spectral characteristic parameters can reflect the energy information transported by each frequency band. Because the brain is a complex non-linear system, the use of non-linear dynamic analysis may also be used to reflect brain states accurately (Janjarasjitt et al 2008;Kang et al 2015;Mikšovský and Raidl 2006). Among the non-linear features, complexity is suitable as it can be calculated within a short time series and fast speed.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: A pilot study

Wang

Yang³

et al. 2020

Preprint

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Background: Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment. Methods:The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics.Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment.Results: For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) ofA band (0.5 -2Hz), accompanied by a significant decrease in the RE ofb band (20 -30Hz). Lempel-Ziv complexity and Co -complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry.Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices. Conclusions:The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

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Testing for nonlinearity in European climatic time series by the method of surrogate data

Cited by 16 publications

References 19 publications

Application and Comparison of Robust Linear Regression Methods for Trend Estimation

Application and Comparison of Robust Linear Regression Methods for Trend Estimation

Testing the performance of three nonlinear methods of time seriesanalysis for prediction and downscaling of European daily temperatures

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: A pilot study

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