Time Series Analysis of Climatic Change
Trends in Temperature and Rainfall
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This book explores the importance of accurate rainfall forecasting for water resource management, agriculture, and disaster preparedness. It presents a comparative analysis of two forecasting models-Support Vector Regression (SVR) and Seasonal Auto Regressive Integrated Moving Average (SARIMA)-using historical rainfall data from 2008 to 2021 to predict trends from 2022 to 2026. Through statistical and visualization techniques such as trend analysis, moving averages, box plots, heatmaps, Z-scores, and density plots, the study identifies patterns and anomalies in rainfall data. While both models...
This book explores the importance of accurate rainfall forecasting for water resource management, agriculture, and disaster preparedness. It presents a comparative analysis of two forecasting models-Support Vector Regression (SVR) and Seasonal Auto Regressive Integrated Moving Average (SARIMA)-using historical rainfall data from 2008 to 2021 to predict trends from 2022 to 2026. Through statistical and visualization techniques such as trend analysis, moving averages, box plots, heatmaps, Z-scores, and density plots, the study identifies patterns and anomalies in rainfall data. While both models show good predictive ability, SVR demonstrates superior performance, especially in capturing complex, non-linear patterns. The book highlights the advantages of integrating machine learning methods with traditional statistical tools to improve rainfall forecasting and support data-driven decisions in agriculture, environmental planning, and climate resilience.
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