UrbanAI 2025 Challenge: Linear vs Transformer Models for Long-Horizon Exogenous Temperature Forecasting
NeutralArtificial Intelligence
- The UrbanAI 2025 Challenge has revealed significant findings in long-horizon exogenous temperature forecasting, comparing linear models such as Linear, NLinear, and DLinear against Transformer-family models including Informer and Autoformer. The study indicates that linear models consistently outperform their more complex counterparts, with DLinear achieving the highest accuracy across all evaluation splits.
- This development underscores the effectiveness of linear models in time series forecasting, particularly in challenging scenarios where only past temperature values are available for predictions. The results suggest that simpler models may still hold significant predictive power, challenging the prevailing trend towards more complex architectures.
- The findings resonate with ongoing discussions in the field of artificial intelligence regarding the balance between model complexity and performance. While Transformer-based models have gained popularity for their versatility, the success of linear models in this context raises questions about the necessity of complex architectures, especially in specific applications like temperature forecasting, where simpler approaches may yield better results.
— via World Pulse Now AI Editorial System
