Multivariate and multi-scale generator based on non-parametric stochastic algorithms
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A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between t...he simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively.
Ključne reči:
cross-correlation / hydrologic time series / non-parametric methods / serial correlation / stochastic data generationIzvor:
Journal of Hydroinformatics, 2019, 21, 6, 1102-1117Napomena:
- This is the submitted version of the article: Đ. Marković, S. Ilić, D. Pavlović, J. Plavšić, and N. Ilich, ‘Multivariate and multi-scale generator based on non-parametric stochastic algorithms’, Journal of Hydroinformatics, vol. 21, no. 6, pp. 1102–1117, Nov. 2019, https://doi.org/10.2166/hydro.2019.071
DOI: 10.2166/hydro.2019.071
ISSN: 1464-7141
WoS: 000504935500010
Scopus: 2-s2.0-85100222208
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Institucija/grupa
GraFarTY - JOUR AU - Marković, Đurica AU - Ilić, Siniša AU - Pavlović, Dragutin AU - Plavšić, Jasna AU - Ilich, Nesa PY - 2019 UR - https://grafar.grf.bg.ac.rs/handle/123456789/1824 AB - A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively. T2 - Journal of Hydroinformatics T1 - Multivariate and multi-scale generator based on non-parametric stochastic algorithms EP - 1117 IS - 6 SP - 1102 VL - 21 DO - 10.2166/hydro.2019.071 ER -
@article{ author = "Marković, Đurica and Ilić, Siniša and Pavlović, Dragutin and Plavšić, Jasna and Ilich, Nesa", year = "2019", abstract = "A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively.", journal = "Journal of Hydroinformatics", title = "Multivariate and multi-scale generator based on non-parametric stochastic algorithms", pages = "1117-1102", number = "6", volume = "21", doi = "10.2166/hydro.2019.071" }
Marković, Đ., Ilić, S., Pavlović, D., Plavšić, J.,& Ilich, N.. (2019). Multivariate and multi-scale generator based on non-parametric stochastic algorithms. in Journal of Hydroinformatics, 21(6), 1102-1117. https://doi.org/10.2166/hydro.2019.071
Marković Đ, Ilić S, Pavlović D, Plavšić J, Ilich N. Multivariate and multi-scale generator based on non-parametric stochastic algorithms. in Journal of Hydroinformatics. 2019;21(6):1102-1117. doi:10.2166/hydro.2019.071 .
Marković, Đurica, Ilić, Siniša, Pavlović, Dragutin, Plavšić, Jasna, Ilich, Nesa, "Multivariate and multi-scale generator based on non-parametric stochastic algorithms" in Journal of Hydroinformatics, 21, no. 6 (2019):1102-1117, https://doi.org/10.2166/hydro.2019.071 . .