Long memory or shifting means in geophysical time series?
In the literature many papers state that long-memory time series models such as Fractional Gaussian Noises (FGN) or Fractionally Integrated series (FI(d)) are empirically indistinguishable from models with a non-stationary mean, but which are mean reverting. We present an analysis of the statistical...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/48365 |
| _version_ | 1848758088909193216 |
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| author | Rea, W. Reale, M. Brown, J. Oxley, Leslie |
| author_facet | Rea, W. Reale, M. Brown, J. Oxley, Leslie |
| author_sort | Rea, W. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In the literature many papers state that long-memory time series models such as Fractional Gaussian Noises (FGN) or Fractionally Integrated series (FI(d)) are empirically indistinguishable from models with a non-stationary mean, but which are mean reverting. We present an analysis of the statistical cost of model mis-specification when simulated long memory series are analysed by Atheoretical Regression Trees (ART), a structural break location method. We also analysed three real data sets, one of which is regarded as a standard example of the long memory type. We find that FGN and FI(d) processes do not account for many features of the real data. In particular, we find that the data sets are not H-self-similar. We believe the data sets are better characterized by non-stationary mean models. © 2010 IMACS. Published by Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T09:38:26Z |
| format | Conference Paper |
| id | curtin-20.500.11937-48365 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:38:26Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-483652018-03-29T09:07:34Z Long memory or shifting means in geophysical time series? Rea, W. Reale, M. Brown, J. Oxley, Leslie In the literature many papers state that long-memory time series models such as Fractional Gaussian Noises (FGN) or Fractionally Integrated series (FI(d)) are empirically indistinguishable from models with a non-stationary mean, but which are mean reverting. We present an analysis of the statistical cost of model mis-specification when simulated long memory series are analysed by Atheoretical Regression Trees (ART), a structural break location method. We also analysed three real data sets, one of which is regarded as a standard example of the long memory type. We find that FGN and FI(d) processes do not account for many features of the real data. In particular, we find that the data sets are not H-self-similar. We believe the data sets are better characterized by non-stationary mean models. © 2010 IMACS. Published by Elsevier B.V. All rights reserved. 2011 Conference Paper http://hdl.handle.net/20.500.11937/48365 10.1016/j.matcom.2010.06.007 restricted |
| spellingShingle | Rea, W. Reale, M. Brown, J. Oxley, Leslie Long memory or shifting means in geophysical time series? |
| title | Long memory or shifting means in geophysical time series? |
| title_full | Long memory or shifting means in geophysical time series? |
| title_fullStr | Long memory or shifting means in geophysical time series? |
| title_full_unstemmed | Long memory or shifting means in geophysical time series? |
| title_short | Long memory or shifting means in geophysical time series? |
| title_sort | long memory or shifting means in geophysical time series? |
| url | http://hdl.handle.net/20.500.11937/48365 |