A Survey of Deep Learning for Time Series Forecasting: Theories, Datasets, and State-of-the-Art Techniques

Deep learning (DL) has revolutionized time series forecasting (TSF), surpassing traditional statistical methods (e.g., ARIMA) and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data. This comprehensive survey reviews state-of-the-art DL architectures for TSF, focusing on four core paradigms: (1) Convolutional Neural Networks (CNNs), adept at extracting localized temporal features; (2) Recurrent Neural Networks (RNNs) and their advanced variants (LSTM, GRU), designed for sequential dependency modeling; (3) Graph Neural Networks (GNNs), specialized for forecasting structured relational data with spatial-temporal dependencies; and (4) Transformer-based models, leveraging self-attention mechanisms to capture global temporal patterns efficiently. We provide a rigorous analysis of the theoretical underpinnings, recent algorithmic advancements (e.g., TCNs, attention mechanisms, hybrid architectures), and practical applications of each framework, supported by extensive benchmark datasets (e.g., ETT, traffic flow, financial indicators) and standardized evaluation metrics (MAE, MSE, RMSE). Critical challenges, including handling irregular sampling intervals, integrating domain knowledge for robustness, and managing computational complexity, are thoroughly discussed. Emerging research directions highlighted include diffusion models for uncertainty quantification, hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability, quantile regression with Transformers for risk-aware forecasting, and optimizations for real-time deployment. This work serves as an essential reference, consolidating methodological innovations, empirical resources, and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.