Time series study of climate variables utilising a seasonal ARIMA technique for the Indian states of Punjab and Haryana

Weather parameters, such as precipitation and temperature predictions, could be advantages in to making decisions, risk management, and water resource optimization. These two variables have an indisputable impact on the hydrological cycle, crop production cycles, agricultural water usage, and the environment. This paper inspects the time series analysis of the monthly precipitation and mean temperatures for the Punjab and Haryana states of India. For Punjab state, data was taken for the years 1951–2020 and for Haryana state, data was collected for the years 1957–2020. In order to anticipate the subsequent 15 years (2016–2030), using the seasonal ARIMA (SARIMA) model and fitted to the data up to the year 2015. For precipitation, ARIMA (1,1,0) (0,1,2) 12 and ARIMA (1,1,2) (0,1,1) 12 were the most suitable model for precipitation data analysis of Haryana and Punjab states, respectively. For temperature, best selected model was ARIMA (2,1,2) (0,1,1) 12, and ARIMA (2,1,2) (2,1,2) 12 for Haryana and Punjab states, respectively. The predicted outcomes establish that the predicted data closely matches the data’s pattern. For certain years, however, overestimations of precipitation in July have been discovered.