Detecting correlations among data
Abstract
Detecting Correlations among Data, develops techniques for quantifying correlations within data sets, and especially within and among time series. Several different manifestations of correlation are explored and linked together: from probability theory, covariance; from time series analysis, cross-correlation; and from spectral analysis, coherence. The effect of smoothing and band-pass filtering on the statistical properties of the data and their spectra is also discussed.
Keywords
covariance; correlation coefficient; autocorrelation; cross-correlation; coherence; delay; lag; ozone; taper; sidelobe
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