Design of an integrated model combining recurrent convolutions and attention mechanism for time series prediction

In applications such as healthcare, finance, and environmental monitoring, the demand for more reliable time-series prediction models has grown critical. Traditional models, such as VARMAx, struggle with capturing non-linear and complex dependencies inherent in sequential data. To address these challenges, this work proposes a hybrid model combining Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) and incorporating attention mechanisms for improved precision and interpretability. LSTM networks are utilized to capture long-term dependencies in sequential data, while CNNs are employed to extract significant local features. The attention mechanism enhances the model's focus on critical time-series instances, improving prediction accuracy and interpretability. Additionally, hyperparameter optimization is achieved using metaheuristic approaches such as the grey wolf optimizer and the coot optimization algorithm, ensuring maximum performance. The model integrates multimodal LSTMs to handle diverse data types, such as text and images, while preserving relationships between entities using Graph Neural Networks (GNNs). Adaptive feedback learning, combining reinforcement and federated learning, allows for real-time model adaptability while maintaining data privacy. Bayesian neural networks with dropout regularization provide uncertainty estimation, delivering confidence intervals alongside predictions. The proposed hybrid model demonstrates a 6-8% absolute improvement in predictive accuracy, reduced RMSE, and enhanced interpretability compared to traditional benchmarks. Its effectiveness is particularly evident in scenarios with high uncertainty, complex data, and the need for real-time model adaptation, setting a new standard in time-series prediction.