A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
Published:Dec 27, 2025 16:25
•1 min read
•r/artificial
Analysis
This survey paper provides a valuable overview of the evolving landscape of deep learning architectures for time series forecasting. It highlights the shift from traditional statistical methods to deep learning models like MLPs, CNNs, RNNs, and GNNs, and then to the rise of Transformers. The paper's emphasis on architectural diversity and the surprising effectiveness of simpler models compared to Transformers is particularly noteworthy. By comparing and re-examining various deep learning models, the survey offers new perspectives and identifies open challenges in the field, making it a useful resource for researchers and practitioners alike. The mention of a "renaissance" in architectural modeling suggests a dynamic and rapidly developing area of research.
Key Takeaways
- •Deep learning is increasingly used for time series forecasting.
- •Transformer models are important but not always the best architecture.
- •Architectural diversity is a key trend in time series forecasting research.
Reference
“Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting.”