• Ganesh Phad
• Aniket Muley

This study concern with the systematic approach for Seasonal Autoregressive Integrated Moving Average (SARIMA) model fitting by dealing with the time series of monthly average minimum and maximum temperature dataset. Here, the data is collected from the Nashik meteorological centre/hydrology project of Maharashtra state in India. Initially, base model has been fitted and simulation technique is used to obtain the best model among the all others. Finally, we have obtained SARIMA(0, 1, 2)(2, 1, 1)12 model for monthly average minimum temperature and SARIMA (1, 1, 2)(3, 1, 1)12 model for monthly average maximum temperature as the optimum models for given study area. The selected optimum models have the highest R-square with lowest Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The residual analysis shows good prediction accuracy of the selected optimum models. In a nutshell, in future, these models can useful to deal with the prediction of maximum and minimum temperature so that, it will be helpful

Time series, temperature, SARIMA, modelling, R-square, RMSE, MAPE, prediction.

" A Systematic Approach for Minimum and Maximum Temperature Prediction Using SARIMA Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.b405-b413, July-2022, Available :http://www.jetir.org/papers/JETIR2207147.pdf

" A Systematic Approach for Minimum and Maximum Temperature Prediction Using SARIMA Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppb405-b413, July-2022, Available at : http://www.jetir.org/papers/JETIR2207147.pdf