Sentences with phrase «time series analysis using»

Data were analyzed through time series analysis using mixed regression.

We give examples of climate time series analyses using the EMD and discuss the problems we encounter in calculation and interpretation of trends extracted from the data limited in time extent.

Not exact matches

Time series analysis could help predict, based on what happens at the London Stock Exchange, how other exchanges will be impacted using decades» worth of stock data.

Individual growth curve models were developed for multilevel analysis and specifically designed for exploring longitudinal data on individual changes over time.23 Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct model for our individual growth curves, we compared a series of MIXED models by evaluating the difference in deviance between nested models.23 Both fixed quadratic and cubic MIXED models fit our data well, but we selected the fixed quadratic MIXED model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.

To assess the influence of phosphorus on nitrogen removal, the researchers used a comparative approach — they examined the differences between how much nitrogen goes into lakes and how much comes out downstream — coupled with time - series analyses of nitrogen and phosphorus concentration in large lakes.

To test their hypothesis, Rampino and Caldeira performed time - series analyses of impacts and extinctions using newly available data offering more accurate age estimates.

Using time series and correlation analyses, she studies how factors such as the glass quality, the age of the tools and their coatings, and the oxygen partial pressure in the machine affect the quality of the finished lens.

Experience in numerical modeling and use of hydro - acoustic, time series analyses, computing skills, and knowledge of Matlab programming will be an asset.

They consider the use and calculation of 3 period moving averages; the influences acting upon sales forecasts; extrapolation; correlation analysis techniques; scatter graphs; an evaluation of time - series analysis methods; the line of best fit; qualitative forecasting methods (Delphi Technique; Brainstorming and Intuition) and an evaluation of qualitative forecasting.

The report, the first of a planned series of analyses of NAEP's background - survey data, looks at how 4th and 8th graders use existing school time, including their attendance, instructional time, and homework.

In general, in time series analysis one should use all the data available.

Mike's work, like that of previous award winners, is diverse, and includes pioneering and highly cited work in time series analysis (an elegant use of Thomson's multitaper spectral analysis approach to detect spatiotemporal oscillations in the climate record and methods for smoothing temporal data), decadal climate variability (the term «Atlantic Multidecadal Oscillation» or «AMO» was coined by Mike in an interview with Science's Richard Kerr about a paper he had published with Tom Delworth of GFDL showing evidence in both climate model simulations and observational data for a 50 - 70 year oscillation in the climate system; significantly Mike also published work with Kerry Emanuel in 2006 showing that the AMO concept has been overstated as regards its role in 20th century tropical Atlantic SST changes, a finding recently reaffirmed by a study published in Nature), in showing how changes in radiative forcing from volcanoes can affect ENSO, in examining the role of solar variations in explaining the pattern of the Medieval Climate Anomaly and Little Ice Age, the relationship between the climate changes of past centuries and phenomena such as Atlantic tropical cyclones and global sea level, and even a bit of work in atmospheric chemistry (an analysis of beryllium - 7 measurements).

Almost all time series analysis assumes an infinate series, but uses finite data.

If it matters at all, I never found a use for FFT in my previous line of work (population ecology), although I did have occasion to use PCA on a time series analysis.

Development, use and application of the HYDROSPECT data analysis system for the detection of changes in hydrological time series for use in WCP - Water and National Hydrological Services: report by Maciej Radzeijewski and Zbigniew W. Kundzewicz.

@DS: This is why you do not smooth a time series then use the smoothed data as input into a subsequent analysis.

On longer time scales, the two longest time series (using independent criteria for selection, quality control, interpolation and analysis of similar data sets) show good agreement about long - term trends and also on decadal time scales.

Well, it's not a traditional time series, and it hasn't used commonly accepted tools of analysis that are well understood by the scientific community to produce a reliable foundation for further work, so as an ensemble it is more infographic than anything else.

«I was irked by the persistent use of wishy - washy terminology such as «likely» and «very likely» that was totally uncalled for... Such «social sciences» terminology might be allowable if there was no other available evidence for global warming except for the statistical analysis of a relatively short global temperature time - series (on which there is superimposed a substantial natural variability component).

This research involved the use of mathematical methods of econometrics specifically designed for structural analysis of time series data.

The main purpose of the first phase (development of the RCPs) is to provide information on possible development trajectories for the main forcing agents of climate change, consistent with current scenario literature allowing subsequent analysis by both Climate models (CMs) and Integrated Assessment Models (IAMs).1 Climate modelers will use the time series of future concentrations and emissions of greenhouse gases and air pollutants and land - use change from the four RCPs in order to conduct new climate model experiments and produce new climate scenarios as part of the parallel phase.

Infact, the procedure of determining the behavior of such processes, a Statistical analytic process titled a «Time series» uses all data points that are collected within the method determined by the pre-procedure of «Experimental Design», made to facilitate the analysis in a manner of known (and best) correlation.

Lastly, a bias - corrected wavelet analysis was used to identify dominant periodicities in the unfiltered mean PDSI time series between the MCA, LIA, and RW [46].

John Imbrie used time - series analysis to statistically compare the timing and cycles in the sea surface temperature and global ice volume records with patterns of the Earth's orbit.

Thin lines in A — C are low - passed filtered time series used in the subsequent analyses (see Materials and Methods).

When a team of Italian researchers asked to use my Chaos analysis software last year to look at a time series of 500 years of averaged South Italian winter temperatures, the opportunity arose to test this.

We applied a low - passed filter to the original, ice core - based temperature and dust time series of Fig. 2 and then used these low - passed filtered data in the subsequent analyses.

Analysis of extended time series data could then be used to improve models (104), e.g., an effort to determine the Atlantic's τ and assimilate it into ocean models could reduce the vast intra - and intermodel (44) spread regarding the proximity to a tipping point (102).

For the analysis of all possible 12 - mo periods, we generate the annual time series of each 12 - mo period (January — December, February — January, etc.) using a 12 - mo running mean.

A good discussion and example using Pacific equatorial SSTs and using PCs to characterize ENSO can be found in Hsieh, W. W. (2004), Nonlinear multivariate and time series analysis by neural network methods, Rev. Geophys., 42, RG1003, doi: 10.1029 / 2002RG000112.

The results from tests of our first hypothesis differ from other studies in which time - series analyses were used to explore the relationship between the secondary sex ratio (SSR) and fluctuations in mean annual ambient temperature.

We are able to use the scalpel approach because our analysis method can use very short records, whereas the methods employed by other groups generally require their time series be long enough to contain a significant reference or overlap interval.

However, studies conducted on a smaller scale in Finland and elsewhere in Scandinavia using long - term data sets and time - series analyses have found that more males are born in years with higher mean annual temperatures [7], [11], [14], which suggests that mean ambient temperature affects maternal condition and influences the SSR.

We tested three main hypotheses using time series analyses.

«Rescaled range analysis is one of the classical methods used for detecting and quantifying long - term dependence in time series.

From reading the entire month long 1500 + comments, many of the staticians providing statistics power to climate science which has linked temperature and CO2 had not considered determining the presence of a unit root in this time series data set, assumed there was not a unit root, and proceeded with using Ordinary Least Square analysis.

Rather than looking at the dataset available in 1998 and using standard time series analysis techniques (available in 1998) to establish its underlying structure and hence the null hypothesis, the choice in null hypothesis was limited to a couple of options (trend vs no - trend) that were particularly naive (probably more naive than human instinct) and a selection process was established that appears to use the skill criteria (exactly what was done is unclear from the text) and inappropriate multiple use of the dataset.

Our approach is anchored in statistical analysis of time - series datasets using models of growth and diffusion, particularly Lotka - Volterra dynamical systems.

Here we consider briefly some additional studies that examine the spatial structure of observed trends or use more sophisticated time - series analysis techniques to characterise the behaviour of global, hemispheric and zonal mean temperatures.

Subsequently it appears that the hockey stick was caused by improper use of Primary Component analysis designed to smooth out a ragged time series.

My earlier research has concerned, among others things, the use and the reliability of LES, the application of nonlinear time series analysis on flow fields, and the inclusion of marine organic aerosol sources in global climate models.

I have a feeling a lot of PhDs could get minted from extending your analysis here alone ---- does it hold for longer time series, using Vostok Ice Cores (not tree rings please!!)

The study then uses analysis of time - series network rankings for each case to determine 1) the age at which cases in the network typically cease to be important, and 2) what characteristics define those cases that continue to be important despite the passage of time.

I have experience as a statistical modeler and analyst developing risk models using multivariate techniques, marketing segmentation using clustering, process analysis using decision tree machine learning techniques, and time series analysis for...

Prepared analysis reports using different statistical tools such as time series analysis and time graphs, in order to understand current market trends.

RMSSD is sensitive to high - frequency heart period fluctuations in the respiratory frequency range and is used as an index of vagal cardiac control.3) In the frequency - domain analysis, the power spectra of three frequency bands are calculated: very low frequency (VLF; 0.005 - 0.040 Hz), low frequency (LF; 0.04 - 0.15 Hz), high frequency (HF; 0.15 - 0.40 Hz), and total power (TP), including VLF, LF, and HF.1) For the non-linear complexity measure, the approximate entropy (ApEn) is calculated.4) The ApEn is a parameter that was developed to quantify the degree of regularity versus unpredictability in a higher dimensional attractor reconstructed from a time series, such as the instantaneous heart rate time series.5, 6) A lower ApEn value reflects a higher degree of regularity, and the higher the entropy value, the more unpredictable the time series.5, 6)

To test this potential indirect effect, we used a non-parametric Monte Carlo simulation method, in which the indirect effect obtained from the a (the link between the predictor variable and the indirect effect variable) and b (the link between the indirect effect variable and the dependent variable, controlling for the remaining predictors) paths in a series of regression analyses is simulated k number of times using the slopes and standard errors obtained from the data (we used k = 50,000).

The SDQ's internal factor structure was assessed by using confirmatory factor analysis, with a series of competing models and extensions used to determine construct, convergent, and discriminant validity and measurement invariance over time.