Name
Abstract | ||
|---|---|---|
Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysisand can even be useful for guiding that analysis. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/e20120931 | ENTROPY |
Keywords | Field | DocType |
paleoclimate,permutation entropy,ice core,anomaly detection | Anomaly detection,Ice core,Paleoclimatology,Outlier,Permutation entropy,Algorithm,Raw data,Polar,Statistics,Conjecture,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 12 | 1099-4300 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joshua Garland | 1 | 9 | 5.25 |
| Tyler R. Jones | 2 | 0 | 0.68 |
| Michael Neuder | 3 | 0 | 0.34 |
| Valerie Morris | 4 | 0 | 0.34 |
| James W. C. White | 5 | 0 | 0.68 |
| Elizabeth Bradley | 6 | 101 | 16.27 |