 |
| Titel |
Efficient Bayesian inference for natural time series using ARFIMA processes |
| VerfasserIn |
T. Graves, R. B. Gramacy, C. L. E. Franzke, N. W. Watkins |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 6 ; Nr. 22, no. 6 (2015-11-18), S.679-700 |
| Datensatznummer |
250121009
|
| Publikation (Nr.) |
 copernicus.org/npg-22-679-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Many geophysical quantities, such as atmospheric temperature, water levels in
rivers, and wind speeds, have shown evidence of long memory (LM). LM implies
that these quantities experience non-trivial temporal memory, which
potentially not only enhances their predictability, but also hampers the detection of
externally forced trends. Thus, it is important to reliably identify whether
or not a system exhibits LM. In this paper we present a modern and systematic
approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in
time series analysis, and of increasing interest in climate science. Unlike
most previous work on the inference of LM, which is frequentist in nature, we
provide a systematic treatment of Bayesian inference. In particular, we
provide a new approximate likelihood for efficient parameter inference, and
show how nuisance parameters (e.g., short-memory effects) can be integrated
over in order to focus on long-memory parameters and hypothesis testing more
directly. We illustrate our new methodology on the Nile water level data and
the central England temperature (CET) time series, with favorable comparison
to the standard estimators. For CET we also extend our method to seasonal
long memory. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|