Hybrid Deep Learning Architectures for Time Series Forecasting
DOI:
https://doi.org/10.15662/IJEETR.2025.0704003Keywords:
Time-Series Forecasting, Hybrid Deep Learning, Recurrent Neural Networks, Convolutional Neural Networks, Attention Mechanisms, Transformers, Multi-Scale Modeling, Graph Neural Networks, Explainable AI, Federated LearningAbstract
Time-series forecasting plays a critical role in numerous domains, including finance, energy, healthcare, and weather prediction. Traditional statistical models like ARIMA have long been used, but they often fall short when dealing with non-linear, complex temporal dependencies. Recently, deep learning architectures have shown significant promise in capturing such complex patterns due to their ability to learn hierarchical feature representations from raw data. However, no single deep learning model architecture is universally optimal for all types of time-series data. This has led to the rise of hybrid deep learning architectures that combine strengths from multiple models to enhance forecasting accuracy and robustness.
Hybrid models typically integrate recurrent neural networks (RNNs), convolutional neural networks (CNNs), and attention mechanisms, enabling them to effectively capture both short-term and long-term dependencies, as well as spatial and temporal correlations. For example, CNN layers are effective in extracting local patterns, whereas RNNs such as LSTM and GRU handle sequential dependencies, while attention modules prioritize important time steps dynamically. This paper reviews state-of-the-art hybrid deep learning architectures proposed in 2024 for time-series forecasting, highlighting innovations in model design, training strategies, and interpretability. We discuss models that integrate transformers with CNN-RNN stacks, multi-scale convolutional filters, and graph neural networks to handle spatial temporal data. Performance improvements over standalone models are evident in benchmarks like energy load forecasting, stock market prediction, and medical signal analysis.
Challenges remain in terms of computational complexity, overfitting on noisy data, and model interpretability. Nonetheless, hybrid deep learning continues to push the boundaries of time-series forecasting accuracy. We conclude with future directions emphasizing explainable AI, federated learning for decentralized time-series data, and efficient model compression to enable real-time deployment.
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