Efficient and reliable forecasting techniques for various climatic conditions are indispensable in agricultural dependent country like India. In this context, rainfall forecasting is one of the most challenging tasks because of the existence of three patterns, viz., temporal, spatial, and non-linear, simultaneously. Space-Time Autoregressive Moving Average (STARMA) model is one of the promising and popular approaches for modelling spatio-temporal time series data. However, the observed features of many space-time rainfall data comprise complex non-linear dynamics and modelling these patterns often go beyond the capability of conventional STARMA model. Moreover, despite the popularity of artificial neural network (ANN) and support vector machine (SVM) for modelling complex non-linear dynamics, they are not capable to deal with spatial patterns. To overcome the problem, a new spatio-temporal hybrid modelling approach has been proposed by integrating STARMA, ANN, and SVM as well. The proposed approach has been empirically illustrated on annual precipitation data of six districts of northern part of West Bengal, India. The study reveals that proposed spatio-temporal hybrid approach has better modelling and forecasting precision over conventional STARMA as well as most widely used Autoregressive Integrated Moving Average (ARIMA) model.

在像印度这样的农业大国中，针对各种气候条件的高效可靠的预测技术是必不可少的。在这种情况下，降雨预报是最具挑战性的任务之一，因为同时存在三种模式，即时间，空间和非线性。时空自回归移动平均线（STARMA）模型是对时空时间序列数据进行建模的有前途和流行的方法之一。但是，许多时空降雨数据的观测特征包括复杂的非线性动力学，对这些模式进行建模通常超出了常规STARMA模型的能力。此外，尽管人工神经网络（ANN）和支持向量机（SVM）可以用于对复杂的非线性动力学建模，但它们无法处理空间模式。为了解决该问题，已经提出了一种新的时空混合建模方法，该方法还集成了STARMA，ANN和SVM。在印度西孟加拉邦北部六个地区的年降水量数据中，以经验方式说明了该方法。研究表明，与传统的STARMA以及最广泛使用的自回归综合移动平均（ARIMA）模型相比，提出的时空混合方法具有更好的建模和预测精度。