Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-679-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-22-679-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Nov 2015
Research article |
| 18 Nov 2015
Efficient Bayesian inference for natural time series using ARFIMA processes
T. Graves
URS Corporation, London, UK
R. B. Gramacy
The University of Chicago, Booth School of Business, Chicago, IL, USA
C. L. E. Franzke
CORRESPONDING AUTHOR
Meteorological Institute and Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany
N. W. Watkins
Centre for the Analysis of Time Series, London School of Economics and Political Science, London, UK
Centre for Fusion Space and Astrophysics, University of Warwick, Coventry, UK
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
Faculty of Mathematics, Computing and Technology, Open University, Milton Keynes, UK
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Cited
18 citations as recorded by crossref.
- Variability and Confidence Intervals for the Mean of Climate Data with Short- and Long-Range Dependence M. Bowers & W. Tung 10.1175/JCLI-D-17-0090.1
- Revealing Higher-Order Interactions in High-Dimensional Complex Systems: A Data-Driven Approach M. Tabar et al. 10.1103/PhysRevX.14.011050
- Bayesian Inference for ARFIMA Models G. Durham et al. 10.1111/jtsa.12443
- Statistical inference of one-dimensional persistent nonlinear time series and application to predictions J. Kassel & H. Kantz 10.1103/PhysRevResearch.4.013206
- Modeling of water usage by means of ARFIMA–GARCH processes J. Gajda et al. 10.1016/j.physa.2018.08.134
- Likelihood Inference for Possibly Non-Stationary Processes via Adaptive Overdifferencing M. Griffin et al. 10.1080/00401706.2025.2453207
- The Structure of Climate Variability Across Scales C. Franzke et al. 10.1029/2019RG000657
- Inferring nonlinear fractional diffusion processes from single trajectories J. Kassel et al. 10.1088/1367-2630/ad091e
- Identification and validation of stable ARFIMA processes with application to UMTS data K. Burnecki & G. Sikora 10.1016/j.chaos.2017.03.059
- Bayesian estimation of Gegenbauer processes R. Hunt et al. 10.1080/00949655.2022.2138883
- Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory H. He et al. 10.1155/2021/5562110
- An approximate fractional Gaussian noise model with $$\mathcal {O}(n)$$ O ( n ) computational cost S. Sørbye et al. 10.1007/s11222-018-9843-1
- Comparison of methods for extracting annual cycle with changing amplitude in climate series Q. Deng & Z. Fu 10.1007/s00382-018-4432-8
- Regional contrasting DTR’s predictability over China S. Fu et al. 10.1016/j.physa.2019.01.077
- Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models T. Graves et al. 10.1016/j.physa.2017.01.028
- Estimation methods for stationary Gegenbauer processes R. Hunt et al. 10.1007/s00362-022-01290-3
- Bayesian estimation of fractional difference parameter in ARFIMA models and its application M. Fazlalipour Miyandoab et al. 10.1016/j.ins.2023.01.108
- Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution R. Oh et al. 10.5351/CSAM.2017.24.5.507
17 citations as recorded by crossref.
- Variability and Confidence Intervals for the Mean of Climate Data with Short- and Long-Range Dependence M. Bowers & W. Tung 10.1175/JCLI-D-17-0090.1
- Revealing Higher-Order Interactions in High-Dimensional Complex Systems: A Data-Driven Approach M. Tabar et al. 10.1103/PhysRevX.14.011050
- Bayesian Inference for ARFIMA Models G. Durham et al. 10.1111/jtsa.12443
- Statistical inference of one-dimensional persistent nonlinear time series and application to predictions J. Kassel & H. Kantz 10.1103/PhysRevResearch.4.013206
- Modeling of water usage by means of ARFIMA–GARCH processes J. Gajda et al. 10.1016/j.physa.2018.08.134
- Likelihood Inference for Possibly Non-Stationary Processes via Adaptive Overdifferencing M. Griffin et al. 10.1080/00401706.2025.2453207
- The Structure of Climate Variability Across Scales C. Franzke et al. 10.1029/2019RG000657
- Inferring nonlinear fractional diffusion processes from single trajectories J. Kassel et al. 10.1088/1367-2630/ad091e
- Identification and validation of stable ARFIMA processes with application to UMTS data K. Burnecki & G. Sikora 10.1016/j.chaos.2017.03.059
- Bayesian estimation of Gegenbauer processes R. Hunt et al. 10.1080/00949655.2022.2138883
- Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory H. He et al. 10.1155/2021/5562110
- An approximate fractional Gaussian noise model with $$\mathcal {O}(n)$$ O ( n ) computational cost S. Sørbye et al. 10.1007/s11222-018-9843-1
- Comparison of methods for extracting annual cycle with changing amplitude in climate series Q. Deng & Z. Fu 10.1007/s00382-018-4432-8
- Regional contrasting DTR’s predictability over China S. Fu et al. 10.1016/j.physa.2019.01.077
- Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models T. Graves et al. 10.1016/j.physa.2017.01.028
- Estimation methods for stationary Gegenbauer processes R. Hunt et al. 10.1007/s00362-022-01290-3
- Bayesian estimation of fractional difference parameter in ARFIMA models and its application M. Fazlalipour Miyandoab et al. 10.1016/j.ins.2023.01.108
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