Abstract – Multiplicative seasonal autoregressive integrated moving average models are appropriate for the monthly stream flow of the Zayandehrud River in western Isfahan province, Iran, through the Box and Jenkins time series modeling approach. Among the selected models interpreted from ACF and PACF, seasonal multiplicative ARIMA (1,1,0) × (0,1,1) satisfied all tests and showed the best performance. Seasonal moving average parameter in the model indicates periodicity, and long memory in the streamflow, while a nonseasonal autoregressive parameter indicates the linearity of the monthly streamflow. The model forecasted streamflow for 24 leading months showed the ability of the model to predict and forecast statistical properties of the str...
Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for repres...
The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in ...
In water resources management, forecasting is an activity that very beneficial for future extension....
This study was designed to find the best stochastic model (using of time series analysis) for annual...
This study aims to develop an improved time series model to overcome difficulties in modeling monthl...
The dynamic and accurate forecasting of monthly streamflow processes of a river are important in the...
Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticat...
Simulation of streamflow is one of important factors in water utilization. In this paper, a linear s...
Precise prediction of the streamflow has a significantly importance in water resources management. I...
AbstractThis paper presents the application of autoregressive integrated moving average (ARIMA), sea...
To solve many problems such as estimation of average monthly river inflow, it is necessary to consid...
Two multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were developed ...
Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for repres...
The monthly and annual time series of the flow of the Tigris River at Al-Amara barrage was analyzed ...
To perform hydrological forecasting, time series methods are often employed. In univariate time seri...
Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for repres...
The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in ...
In water resources management, forecasting is an activity that very beneficial for future extension....
This study was designed to find the best stochastic model (using of time series analysis) for annual...
This study aims to develop an improved time series model to overcome difficulties in modeling monthl...
The dynamic and accurate forecasting of monthly streamflow processes of a river are important in the...
Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticat...
Simulation of streamflow is one of important factors in water utilization. In this paper, a linear s...
Precise prediction of the streamflow has a significantly importance in water resources management. I...
AbstractThis paper presents the application of autoregressive integrated moving average (ARIMA), sea...
To solve many problems such as estimation of average monthly river inflow, it is necessary to consid...
Two multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were developed ...
Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for repres...
The monthly and annual time series of the flow of the Tigris River at Al-Amara barrage was analyzed ...
To perform hydrological forecasting, time series methods are often employed. In univariate time seri...
Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for repres...
The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in ...
In water resources management, forecasting is an activity that very beneficial for future extension....