Vol. 7, Issue 1 (2019)
Time series analysis model for forecasting of temperature at Allahabad region
Author(s): Anosh Graham, Jyotish Kumar Sahu, Yogeshwar Kumar Sahu and Avinash Yadu
Abstract: The prediction of temperature 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 temperature 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 temperature on monthly scales. seasonal ARIMA (1, 0, 0) (0, 1, 1) maximum temperature (1,0,1) (0,1,1) minimum temperature was identified the best model to forecast temperature 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 satis factory predictions for temperature parameters on monthly scale.
Related Graphics: Click here for more related graphics
Fig. 1: Observed temperature (maximum, minimum) in Allahabad district (Jan. 1985-Dec. 15)
Fig. 2: Autocorrelation function of Maximum temperature
Fig. 3: Partial autocorrelation function of Maximum temperature
Pages: 882-886 | 419 Views 60 Downloads
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How to cite this article:
Anosh Graham, Jyotish Kumar Sahu, Yogeshwar Kumar Sahu, Avinash Yadu. Time series analysis model for forecasting of temperature at Allahabad region. Int J Chem Stud 2019;7(1):882-886.
