Pharmacognosy

Journal of Pharmacognosy and Phytochemistry

  • Printed Journal
  • Indexed Journal
  • Refereed Journal
  • Peer Reviewed Journal
Login

NAAS Rating: 5.21

updates
NAAS Rating: 5.21 new

Journal of Pharmacognosy and Phytochemistry

Vol. 6, Issue 5 (2017)

Time series analysis model to forecast rainfall for Allahabad region

Author(s): Anosh Graham and Ekta Pathak Mishra
Abstract: The prediction of Rainfall on monthly and seasonal time scales is not only scientifically Challenging but is also important for planning and devising agricultural strategies. Various research groups attempted to predict rainfall on a seasonal time scales using different techniques. This paper describes the Box-Jenkins time series seasonal ARIMA (Auto Regression Integrated Moving Average) approach for prediction of rainfall on monthly scales. Seasonal ARIMA model (0, 0, 0) (0, 1, 0) for rainfall was identified the best model to forecast rainfall for next 5 year’s with confidence level of 95 percent by analyzing last 31 year’s data (1985-20015). Previous years data is used to formulate the seasonal ARIMA model and in determination of model parameters. The performance evaluations of the adopted models are carried out on the basis of correlation coefficient (R2) and root mean square error (RMSE).The study conducted at Allahabad, Uttar Pradesh (India). The results indicate that the seasonal ARIMA model provide consistent and satisfactory predictions for rainfall parameters on monthly scale.
Pages: 1418-1421  |  501 Views  21 Downloads
How to cite this article:
Anosh Graham, Ekta Pathak Mishra. Time series analysis model to forecast rainfall for Allahabad region. J Pharmacogn Phytochem 2017;6(5):1418-1421.
Journal of Pharmacognosy and Phytochemistry