Wavelet-SARIMA-Transformer: A Hybrid Model for Rainfall Forecasting
小波–SARIMA–Transformer：降雨预测的混合模型AI 解读 ↓
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小波–SARIMA–Transformer：降雨预测的混合模型
- 研究问题
- 如何结合多尺度分解、季节性线性模型与深度学习，预测印度东北部月度降雨，并比较不同小波及混合方案的样本外表现？
- 方法与思路
- 作者用最大重叠离散小波变换分解降雨序列，分别由 SARIMA 和 Transformer 建模线性季节与非线性成分，再逆变换重建预测。
- 材料陈述的贡献
- 摘要称，在五个地区及所设训练测试划分下，Haar 小波混合模型的多项预测指标优于比较模型，并提供残差检验和图形评估。
- 阅读关注点
- 应核对分解是否引入未来信息、训练测试是否按时间划分，以及调参、预测跨度和基准模型设置，并查看滚动评估与误差不确定性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未给出具体误差或评估细节，优越性与无偏预测均属作者报告。
原文摘要
This study develops and evaluates a novel hybridWavelet SARIMA Transformer, WST framework to forecast using monthly rainfall across five meteorological subdivisions of Northeast India over the 1971 to 2023 period. The approach employs the Maximal Overlap Discrete Wavelet Transform, MODWT with four wavelet families such as, Haar, Daubechies, Symlet, Coiflet etc. to achieve shift invariant, multiresolution decomposition of the rainfall series. Linear and seasonal components are modeled using Seasonal ARIMA, SARIMA, while nonlinear components are modeled by a Transformer network, and forecasts are reconstructed via inverse MODWT. Comprehensive validation using an 80 is to 20 train test split and multiple performance indices such as, RMSE, MAE, SMAPE, Willmotts d, Skill Score, Percent Bias, Explained Variance, and Legates McCabes E1 demonstrates the superiority of the Haar-based hybrid model, WHST. Across all subdivisions, WHST consistently achieved lower forecast errors, stronger agreement with observed rainfall, and unbiased predictions compared with stand alone SARIMA, stand-alone Transformer, and two-stage wavelet hybrids. Residual adequacy was confirmed through the Ljung Box test, while Taylor diagrams provided an integrated assessment of correlation, variance fidelity, and RMSE, further reinforcing the robustness of the proposed approach. The results highlight the effectiveness of integrating multiresolution signal decomposition with complementary linear and deep learning models for hydroclimatic forecasting. Beyond rainfall, the proposed WST framework offers a scalable methodology for forecasting complex environmental time series, with direct implications for flood risk management, water resources planning, and climate adaptation strategies in data-sparse and climate-sensitive regions.