The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation
Open accessAbstract
Since Hurst[1951] detected the presence of long‐term persistence in hydrologic data, new estimation methods and long‐memory models have been developed. The lack of flexibility in representing the combined effect of short and long memory has been the major limitation of stochastic models used to analyze hydrologic time series. In the present paper a fractionally differenced autoregressive integrated moving average (FARIMA) model is considered. In contrast to using traditional ARIMA models, this approach allows the modeling of both short‐ and long‐term persistence in a time series. A framework for identification and estimation is presented. The data do not have to be Gaussian. The resulting model, which replicates the sample probability density of the data, can be used for the generation of long synthetic series. An application to the monthly and daily inflows of Lake Maggiore, Italy, is presented.
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Cites
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Self-citationAN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
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Cited by
Understanding flood regime changes in Europe: a state-of-the-art assessment
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriaučiūnienė, J., Kundzewicz, Z. W., Lang, M., et al. (2014) · Hydrology and Earth System Sciences · doi:10.5194/hess-18-2735-2014 · Cited by 620
Self-citationStatistical analysis of hydroclimatic time series: Uncertainty and insights
Koutsoyiannis, D., & Montanari, A. (2007) · Water Resources Research · doi:10.1029/2006wr005592 · Cited by 304
Self-citationSalas, J. D., Govindaraju, R. S., Anderson, M., Arabi, M., Francés, F., Suarez, W., Lavado-Casimiro, W. S., & Green, T. R. (2014) · Modern Water Resources Engineering · doi:10.1007/978-1-62703-595-8_1 · Cited by 290
Montanari, A. (2005) · Water Resources Research · doi:10.1029/2004wr003826 · Cited by 231
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Metadata
- Authors
- Montanari, A., Rosso, R., & Taqqu, M. S.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 1997-05
- Volume
- 33
- Issue
- 5
- Pages
- 1035-1044
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W1984169787
- Cited by
- 289
- References
- 21
- FWCI
- 6.63
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
