Times Series Forecasting of Monthly Rainfall using Seasonal Auto Regressive Integrated Moving Average with EXogenous Variables (SARIMAX) Model

Shahenaz Mulla, Chaitanya B. Pande () and Sudhir K. Singh
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Shahenaz Mulla: India Meteorological Department (IMD)
Chaitanya B. Pande: Al-Ayen University
Sudhir K. Singh: K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 6, No 1, 1825-1846

Abstract: Abstract In this study, the monthly rainfall time series forecasting was investigated based on the effectiveness of the Seasonal Auto Regressive Integrated Moving Average with EXogenous variables (SARIMAX) model in the coastal area of Phaltan, taluka. Rainfall forecasting is so much helpful to crops and disaster planning and development during monsoon season. The performance of model was assessed using various statistical metrics such as coefficient of determination (R2), and root mean squared error (RMSE). In this study, we have used multi-dimensional components as inputs in the SARIMAX model for prediction of monthly rainfall. In this work, we have tested two models such as first SARIMAX model orders are (1, 0, 1) and (0, 1, 0, 12), while the second model had orders of (1, 1, 1) and (1, 1, 1, 12). The results of two models have been compared and the performance of model show that the first model outperformed on the rainfall forecasting. The RMSE and R2 performance are 54.54 and 0.91 of first model, respectively, while the second model accuracy is RMSE of 71.12 and an R2 of 0.81. Hence best SARIMAX model has been used for forecasting of monthly time series rainfall from 2020 to 2025 for study area. The results of rainfall data analysis of climatic data are valuable for understanding the variations in global climate change.

Keywords: SARIMAX model; AIC; PACF; Rainfall; Times series forecasting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03756-5

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