Prediction of meteorological parameters using statistical time series models: a case study
by Naba Krushna Sabat; Rashmiranjan Nayak; Harshit Srivastava; Umesh Chandra Pati; Santos Kumar Das
International Journal of Global Warming (IJGW), Vol. 31, No. 1, 2023

Abstract: Natural calamities are frequent nowadays due to global warming caused by the adverse impact created by unsustainable development and associated environmental pollution. Atmospheric weather is highly influenced by global warming. Hence, the present work predicts five important meteorological parameters responsible for weather conditions, such as temperature, humidity, pressure, wind speed, and wind direction of Bengaluru City, from the respective historical data available from January 2009 to January 2020, using statistical time series forecasting models. The comparative analysis of these statistical models shows that the vector auto-regressive moving average model outperforms other models in predicting all the above mentioned parameters.

Online publication date: Wed, 20-Sep-2023

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