Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)

The article discusses the effectiveness of Transformer architectures for time series forecasting, with a focus on the Autoformer model. Autoformer incorporates a seasonal decomposition mechanism and an attention-based architecture, resulting in improved performance on benchmark datasets such as M4 and ETTh1. This advancement is significant for practitioners as it demonstrates the potential of leveraging Transformers, traditionally used in NLP, for time series tasks, potentially enhancing forecasting accuracy and efficiency in various applications.