How do Transformer models handle time series forecasting?

Transformers model time series using self-attention to capture dependencies across all time steps simultaneously.

Transformers replace recurrence with self-attention mechanisms, enabling parallel processing of time steps. Positional encoding is used to inject temporal order. They excel in capturing long-range dependencies and complex seasonal patterns. However, they require large datasets and are computationally expensive.