JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Climate change projection using time series
Chandana K R, Raghavendra S P
The challenging issue that the world is facing is Global climate change. Environmental protection, agricultural productivity, and social development are the accurate prediction of climate change. To estimate the global climate change the study investigates the use of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The technique which is used to detect the seasonal pattern and non- linear properties accurately in climate data is called the SARIMA model. The line of action such as data cleansing, missing value management converts the data into an analysis ready format which is considered as a data pretreatment process. Taking into the consideration of seasonality and autocorrelation of the climate data the SARIMA model is created. To predict the future global climate, change the SARIMA model is tested by historical climate dataset. The prediction performance is inspected through the SARIMA models. As the SARIMA model predict the global climate change by representing the pattern dynamically. The system results in usefulness and accuracy in predicting the global climate change. The SARIMA model speculates the exact prediction of global climate change and helps in decreasing the mortality from cold waves.
ARIMA, SARIMA, Autocorrelation