Predicting daily streamflow with a novel multi-regime switching ARIMA-MS-GARCH model

Wang, Huimin; Song, Songbai; Zhang, Gengxi; Ayantoboc, Olusola O.

doi:10.1016/j.ejrh.2023.101374

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the model was introduced as an application to economic data, the range of applications has expanded significantly since the first publication. These fields include finance (Buffington & Elliott, 2002), political science (Brandt et al, 2014), hydrology (Wang et al, 2023), epidemiology (Shiferaw, 2021) and even bibliometrics (Delbianco et al, 2020).…”

Section: Statement Of Needmentioning

confidence: 99%

MarSwitching.jl: A Julia package for Markov switching dynamic models

Dadej

2024

JOSS

View full text Add to dashboard Cite

Section: Statement Of Needmentioning

confidence: 99%

MarSwitching.jl: A Julia package for Markov switching dynamic models

Dadej

2024

JOSS

View full text Add to dashboard Cite

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Kenyi,

Yamamoto

2024

Discov Appl Sci

View full text Add to dashboard Cite

Accurate river streamflow forecasting is pivotal for effective water resource planning, infrastructure design, utilization, optimization, and flood planning and warning. Streamflow prediction remains a difficult task due to several factors such as climate change, topography, and lack of observed data in some cases. This paper investigates and evaluates the individual performances of the seasonal auto-regressive integrated moving average (SARIMA) and Prophet models in forecasting the streamflow of the Sobat River and proposes a hybrid SARIMA-Prophet model to leverage the strengths of both approaches. Using the augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, the flow of the Sobat River was found to be stationary. The performance of the models was then assessed based on their residual errors and predictive accuracy using the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Residual analysis and prediction capabilities revealed that Prophet slightly edged SARIMA in terms of prediction efficacy; however, both models struggled to effectively capture extreme values, resulting in significant overestimations and slight underestimations. The hybrid SARIMA-Prophet model significantly reduced residual variability, achieving a lower MAE of 4.047 m3/s, RMSE of 6.17 m3/s, and a higher R2 of 0.92 than did the SARIMA (MAE: 5.39 m3/s, RMSE: 8.70 m3/s, R2: 0.85) and Prophet (MAE: 5.35 m3/s, RMSE: 8.32 m3/s, and R2: 0.86) models. This indicates that the hybrid model handles both long-term patterns and short-term fluctuations more effectively than the individual models. The findings of the present study highlight the potential of hybrid SARIMA-Prophet models for streamflow forecasting in terms of accuracy and reliability, thus contributing to more effective water resource management and planning, particularly in the Sobat River.

show abstract

Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Chen,

Shan,

Tang

et al. 2023

Humanit Soc Sci Commun

View full text Add to dashboard Cite

Interest rate market risk faced by China’s commercial banks is increasing after the announcement that the interest rate marketisation is completed. This paper examines the Value-at-Risk, and statistical properties in the daily price return of Shanghai banks’ overnight offered rate. The study applies two-stage approaches, combining GARCH-type models with extreme value theory. Firstly, the Markov regime switching model is used to test the regime states of the series. Secondly, the performance of different VaR models are examined. Results show that the extreme value approach estimates better at the 99% confidence level. The EGARCH-GED model is the most suitable of the employed GARCH-type models. The back-testing results support the idea that the approach used in this study is appropriate for improving commercial banks’ daily risk management. This paper applies the GARCH-EVT method for interest rate measurement after China’s interest rate marketisation and added regime analysis of interest rate. Suggested policy implications will help formulate policies that guide the activities of commercial banks in China.

show abstract

Predicting daily streamflow with a novel multi-regime switching ARIMA-MS-GARCH model

Cited by 4 publications

References 41 publications

MarSwitching.jl: A Julia package for Markov switching dynamic models

MarSwitching.jl: A Julia package for Markov switching dynamic models

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Contact Info

Product

Resources

About