Transformers for Forecasting in AI & Artificial Intelligence

Traditional recurrent models (LSTMs) compress the entire past into a single hidden state. Self-Attention works differently: it calculates a 'relevance score' between every time step in the input. When predicting a specific future moment, the model can look back across the entire historical window and selectively focus on the most important periods—even if they occurred hundreds of steps ago—without losing any detail.

Because Transformers process the entire sequence in parallel (not step-by-step), they have no inherent sense of time or order. We fix this with Positional Encoding. We add a unique mathematical 'signature' to each data point that represents its position in the sequence. This 'map' allows the attention mechanism to understand that point A came before point B, preserving the temporal structure while benefiting from parallel processing speed.

While standard Transformers were built for text, Temporal Fusion Transformers (TFT) are built for time. They include specialized layers for handling Exogenous Variables (like weather affecting sales) and 'Gated Residual Networks' that allow the model to skip irrelevant features. These architectures currently represent the State of the Art (SOTA) for high-stakes, multi-horizon forecasting in industry.