2012
DOI: 10.1007/978-94-007-2745-8_16
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Autocorrelogram and Periodogram Analyses of Palaeolimnological Temporal-Series from Lakes in Central and Western North America to Assess Shifts in Drought Conditions

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Cited by 11 publications
(4 citation statements)
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“…Irregular intervals between samples means that the timeseries analysis methods of autoregressive or moving average processes, in the form of autoregressive integrated moving average (ARIMA) models, are practically impossible to apply because software typically requires even spacing of observations in time. Dutilleul et al (2012) and Birks (2012a), eschewing the term time series, prefer to call such data temporal series on account of the irregular spacing of samples, a distinction that I find unnecessary, however.…”
Section: Introductionmentioning
confidence: 99%
“…Irregular intervals between samples means that the timeseries analysis methods of autoregressive or moving average processes, in the form of autoregressive integrated moving average (ARIMA) models, are practically impossible to apply because software typically requires even spacing of observations in time. Dutilleul et al (2012) and Birks (2012a), eschewing the term time series, prefer to call such data temporal series on account of the irregular spacing of samples, a distinction that I find unnecessary, however.…”
Section: Introductionmentioning
confidence: 99%
“…For the diatom assemblages, we tested the relationship between assemblage and both tidal disturbance (Ti influx) and local habitat change driven by the changing dominance of mangrove vs. microbial mat substrata (d 13 C). The best model was identified using the Akaike Information Criterion (AIC) and the residuals of the best model were checked for temporal autocorrelation using Moran's I statistic (Dutilleul et al 2012). To do this we calculated the Moran's I correlation of the residuals at 20-year increments and then fitted an exponential curve to these estimates to model the degree of autocorrelation in the samples (see Appendix C).…”
Section: Statistical Approachesmentioning
confidence: 99%
“…Recent palynological examples include Willis et al (1999) and paleolimnological examples are reviewed by Dutilleul et al (2012) and Cumming et al (2012). It estimates the proportion of the variance that can be attributed to each of a continuous range of frequencies.…”
Section: Time Series Analysismentioning
confidence: 99%
“…Conventional time series analysis makes stringent assumptions of the data, namely, that the intersample intervals are constant and that the data are stationary, and thus, there are no trends in mean or variance in the time series (Dutilleul et al, 2012). In the absence of equally spaced samples, the usual procedure is to interpolate samples to equal time intervals.…”
Section: Time Series Analysismentioning
confidence: 99%