This study utilizes time series analysis and machine learning techniques to model and forecast rainfall patterns across different seasons in India. The statistical models, i.e., autoregressive integrated moving average (ARIMA) and state space model and machine learning models, i.e., Support Vector Machine, Artificial Neural Network and Random Forest Model were developed and their performance was compared against XGBoost, an advanced machine learning algorithm, using training and testing datasets. The results demonstrate the superior accuracy of XGBoost compared to the statistical models in capturing complex nonlinear rainfall patterns. While ARIMA models tend to overfit the training data, state space models prove more robust to outliers in the testing set. Diagnostic checks show the models adequately capture the time series properties. The analysis indicates essential unchanging rainfall patterns in India for 2023–2027, with implications for water resource management and climate-sensitive sectors like agriculture and power generation. Overall, the study highlights the efficacy of modern machine learning approaches like XGBoost for forecasting complex meteorological time series. The framework presented enables rigorous validation and selection of optimal techniques. Further applications of such sophisticated data analysis can significantly enhance planning and research on the Indian monsoons amidst climate change challenges.

这项研究利用时间序列分析和机器学习技术来建模和预测印度不同季节的降雨模式。开发了统计模型，即自回归积分移动平均（ARIMA）和状态空间模型以及机器学习模型，即支持向量机、人工神经网络和随机森林模型，并将其性能与先进的机器学习算法XGBoost进行了比较，使用训练和测试数据集。结果表明，与统计模型相比，XGBoost 在捕获复杂的非线性降雨模式方面具有更高的准确性。虽然 ARIMA 模型往往会过度拟合训练数据，但事实证明，状态空间模型对于测试集中的异常值更加稳健。诊断检查表明模型充分捕获了时间序列属性。分析表明，2023 年至 2027 年印度降雨模式基本不变，这对水资源管理以及农业和发电等气候敏感部门产生影响。总体而言，该研究强调了 XGBoost 等现代机器学习方法在预测复杂气象时间序列方面的功效。所提出的框架可以严格验证和选择最佳技术。此类复杂数据分析的进一步应用可以显着加强在气候变化挑战下对印度季风的规划和研究。