Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model
ABSTRACT Streamflow (Qflow) process is one of the complex stochastic processes in the hydrology cycle owing to its associated non‐linearity and non‐stationarity characteristics. It is an essential hydrological process to address the complex time series nonlinear phenomena. In this research, a novel...
Saved in:
| Main Authors: | Bugrayhan Bickici, Beste Hamiye Beyaztas, Zaher Mundher Yaseen, Ufuk Beyaztas, Ercan Kahya |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Journal of Flood Risk Management |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/jfr3.70009 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Stochastic Volatility Modeling of Daily Streamflow Time Series
by: Huimin Wang, et al.
Published: (2023-01-01) -
Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index
by: Yenni Angraini, et al.
Published: (2024-05-01) -
Bagging-based heteroscedasticity-adjusted ridge estimators in the linear regression model
Published: (2025-07-01) -
Methodological insights regarding the impact of COVID-19 dataset on stock market performance in African countries: A computational analysis
by: Rhoda Ifeanyi Benson, et al.
Published: (2024-10-01) -
Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models
by: Cuiping Wang, et al.
Published: (2024-12-01)