Weather forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of weather systems remains a challenge for traditional statistical models. Apart from Auto Regressive time forecasting models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU networks), have shown promise in improving forecasting accuracy by capturing temporal dependencies. This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), to automate the search for optimal hyperparameters in these model architectures. Metaheuristic algorithms excel in global optimization, offering robustness, versatility, and scalability in handling non-linear problems. We present a comparative analysis of different model architectures integrated with metaheuristic optimization, evaluating their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate the potential of metaheuristic algorithms in enhancing weather forecasting accuracy \& helps in determining the optimal set of hyper-parameters for each model. The paper underscores the importance of harnessing advanced optimization techniques to select the most suitable metaheuristic algorithm for the given weather forecasting task.

翻译：天气预报在众多领域中扮演着关键角色，但准确捕捉天气系统的复杂动态仍然是传统统计模型面临的挑战。除ARIMA等自回归时间预测模型外，深度学习技术（如常规人工神经网络、长短期记忆网络和门控循环单元网络）通过捕捉时间依赖性在提升预测精度方面展现出潜力。本文探索了元启发式算法（即遗传算法、差分进化算法和粒子群优化算法）在自动化搜索这些模型架构中最优超参数方面的应用。元启发式算法在全局优化方面表现卓越，具有鲁棒性、通用性和可扩展性，能够处理非线性问题。我们提出了集成元启发式优化的不同模型架构的对比分析，并基于均方误差和平均绝对百分比误差等指标评估了其在天气预报中的性能。结果表明，元启发式算法在提升天气预报精度及确定各模型最优超参数集合方面具有潜力。本文强调了利用先进优化技术为给定天气预报任务选择最合适元启发式算法的重要性。