| Autor: |
Shad, Mohammad, Sharma, Y. D., Narula, Pankaj |
| Předmět: |
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| Zdroj: |
Pure & Applied Geophysics; Jan2023, Vol. 180 Issue 1, p405-419, 15p |
| Abstrakt: |
Monsoon rainfall has always been a key driver of the Indian economy. Understanding the spatiotemporal rainfall pattern and its prediction is therefore of great importance. The present endeavor implements a beta seasonal autoregressive moving average (BSARMA) model to forecast the southwest monsoon rainfall (June–September) in five homogeneous regions of the Indian subcontinent. The southwest monsoon rainfall data set for the period 1871–2016 was utilized to conduct the analysis. The best-fit model for each region was selected on the basis of diagnostic analysis and the Akaike information criterion (AIC). Further, the conditional maximum likelihood estimator was employed to compute the parameters of the models. The accuracy of the models was assessed using root mean square error (RMSE), mean absolute error (MAE), and mean absolute scaled error (MASE). The study demonstrates that the BSARMA model outperforms the seasonal autoregressive integrated moving average (SARIMA) model in all three measures. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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