Transformers Effective for Time Series Forecasting — Autoformer outperforms DLinear

AI Impact Summary

Transformers, as demonstrated by Autoformer, offer a compelling alternative to traditional time series forecasting models like DLinear. Empirical results show that Autoformer consistently outperforms DLinear across multiple datasets, leveraging a decomposition layer and an auto-correlation mechanism to capture complex temporal patterns. This suggests that transformer architectures can effectively model time series data, particularly when incorporating domain-specific features like seasonality and trend.