scholarly journals Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model

2017 ◽  
Vol 13 (12) ◽  
pp. 145 ◽  
Author(s):  
Rana Muhammad Adnan ◽  
Xiaohui Yuan ◽  
Ozgur Kisi ◽  
Yanbin Yuan
Keyword(s):  
Moving Average ◽  
Absolute Error ◽  
Information Criteria ◽  
Ar Model ◽  
Coefficient Of Determination ◽  
Streamflow Forecasting ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Auto Regressive

Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site.

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Forecasting Area, Production and Yield of Onion in Bangladesh by Using ARIMA Model

2019 ◽  
pp. 1-12
Author(s):  
Farhana Arefeen Mila ◽  
Mst. Tania Parvin
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Upward Trend ◽  
Average Model ◽  
Domestic Markets ◽  
Moving Average Model ◽  
Auto Regressive ◽  
The Government ◽  
Day By Day

In Bangladesh, onion is the widely used spices both for preparing food and curing diseases as it has medicinal values. As the demand for onion is increasing day by day, it is necessary to make actual projections of onion for undertaking some policies based on it. Therefore, the study investigates the future changes in the area, yield and production of onion in Bangladesh by using the most popular Box-Jenkins methodology. The auto regressive integrated moving average model has been used to understand the pattern of change over a period of 57 years (1961 to 2017) as well as to forecast the changes in the upcoming years. Some information criteria (such as AIC, AICc and BIC) was considered for selecting the best-fitted models of each variable. The forecasted results showed an upward trend for all the variables considered in this study. It implies that the area of onion will increase from 193932.6 hectares in 2018 to 265770.9 hectare in 2027. Again, the amount of onion production will increase from 2073.61 M tons to 3574.06 M tons and for onion yield, it will rise from 10343.17 Kg/ha to 12988.02 kg/ha from 2018 to 2027. These predictions may help the government balancing the demand with the supply and also regulating the price of onion in the domestic markets of Bangladesh.

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