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Abstract
ABSTRACT
In this paper, seasonal autoregressive integrated moving average (SARIMA) model is developed to predict monthly rainfall in Cumilla using data for the period 1971 to 2017.The stationary condition of the data series are observed by ACF and PACF plots and then checked using the statistic such as Augmented Dickey-Fuller Test (ADF). The ADF test confirms that the monthly rainfall is stationary because the p-value of 0.01 is less than 0.05.The model for which the values of the criteria are smallest is considered as the best model. We found that the SARIMA (1,0,0)(2,1,0)[12]has been fitted to the data based on the Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICC) and Schwarz Bayesian Information Criterion (BIC). Using Root Mean Square Error (RMSE), Absolute Mean Error (AME) and Mean Absolute Percentage Error (MAPE) to measure forecast accuracy. Then forecast of the data have been made using selected type of SARIMA model for the next five years.
References:-
References
Akaike H. (1978) A Bayesian analysis of the minimum AIC procedure.
Box G.E.P. and Jenkins G.M. (1976). Time series Analysis, Forecasting and Control. San Francisco: Holden-Day.
Box, George E. P. (2015). Time Series Analysis: Forecasting and Control. WILEY. ISBN 978-1-118-67502-1.
Hyndman, Rob J; Athanasopoulos, George. 8.9 Seasonal ARIMA models. Forecasting: principles and practice. Texts. Retrieved 19 May 2015.
http://hikersbay.com/climate/bangladesh/comilla?lang=en.
Ishtiak Mahmud and et.al (2017), “Monthly rainfall forecast of Bangladesh using autoregressive integrated moving average method”, Environmental Engineering Research; 22(2): 162-168.
J. C. Ramesh Reddy and et.al (2017), Study Forecasting of Monthly Mean Rainfall in Coastal Andhra, International Journal of Statistics and Applications, 7(4): 197-204.
J. C. Paul and et.al (2013), Selection of Best ARIMA Model for Forecasting Average Daily Share Price Index of Pharmaceutical Companies in Bangladesh: A Case Study on Square Pharmaceutical Ltd.; Global Journal of Management and Business Research Finance ,Volume 13 Issue 3 Version 1.0,15-25.
Ljung G. M. and Box G. E. P. (1978). On a Measure of Lack of Fit in Time Series Models. Biometrika. 65: 297.
Md. Mahsin and et.al (2012), “Modeling Rainfall in Dhaka Division of Bangladesh using Time Series Analysis”Journal of Mathematical Modelling and Application , Vol. 1, No.5, 67-73.
“Notation for ARIMA models” Time Series Forecasting System. SAS Institute. Retrieved 19 May 2015.
Samuel Olorunfemi Adams and et.al (2020), Modeling and Forecasting Seasonal Behavior of Rainfall in Abuja, Nigeria; A SARIMA Approach American Journal of Mathematics and Statistics, 10(1): 10-19.
Samuel Olorunfemi Adams and et.al (2019), “Seasonal Autoregressive Integrated Moving Average (SARIMA) Model for the Analysis of Frequency of Monthly Rainfall in Osun State, Nigeria”, Physical Science International Journal, 22(4): 1-9.
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