What is attention mechanism in time series forecasting?

Attention allows models to dynamically focus on relevant time steps when forecasting future values.

Attention mechanisms compute weighted importance scores for different time steps in a sequence. Instead of treating all past values equally, the model learns which timestamps are most relevant for prediction. Self-attention enables capturing long-range dependencies efficiently, forming the basis of Transformer-based time series models.