Articles | Volume 6, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, 2020
https://doi.org/10.5194/ascmo-6-159-2020
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, 2020
https://doi.org/10.5194/ascmo-6-159-2020
 
12 Oct 2020
12 Oct 2020

Comparing climate time series – Part 1: Univariate test

Timothy DelSole and Michael K. Tippett

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Cited articles

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C.: Time Series Analysis: Forecasting and Control, Wiley-Interscience, 4th Edn., 2008. a, b, c, d
Brockwell, P. J. and Davis, R. A.: Time Series: Theory and Methods, Springer Verlag, 2nd Edn., 1991. a
Brockwell, P. J. and Davis, R. A.: Introduction to Time Series and Forecasting, Springer, 2002. a
Buckley, M. W. and Marshall, J.: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review, Rev. Geophys., 54, 5–63, https://doi.org/10.1002/2015RG000493, 2016.  a
Coates, D. S. and Diggle, P. J.: Test for comparing two estimated spectral densities, J. Time Ser. Anal., 7, 7–20, 1986. a, b
Short summary
Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.