Authors:
Anuvab Sen
1
;
Arul Mazumder
2
;
Dibyarup Dutta
3
;
Udayon Sen
4
;
Pathikrit Syam
1
and
Sandipan Dhar
5
Affiliations:
1
Electronics and Telecommunication, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
;
2
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, U.S.A.
;
3
Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
;
4
Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
;
5
Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal, India
Keyword(s):
Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Meta-Heuristics, Artificial Neural Network, Long Short Memory Networks, Gated Recurrent Unit, Auto-Regressive Integrated Moving Average.
Abstract:
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 Mea
n 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.
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