Temporal Fusion Transformer Interpretability

TFT explicitly separates static metadata (time-invariant features like product category or sensor location) from dynamic inputs. Static covariates are processed through dedicated GRN-based encoders that generate context vectors used to modulate the behavior of the temporal processing layers. These context vectors control:

• Variable selection weights for dynamic inputs
• Temporal processing in the LSTM encoder
• Static enrichment of the attention layer This design makes it possible to trace how a static attribute (e.g., store location) systematically influences the model's sensitivity to different time-varying features.

Rather than producing a single point forecast, TFT outputs predictions at multiple quantile levels (e.g., 10th, 50th, 90th percentiles) simultaneously. This is achieved by minimizing the pinball loss across all specified quantiles during training. The resulting prediction intervals provide a built-in measure of aleatoric uncertainty that varies across the forecast horizon. Interpretability is enhanced because analysts can observe not just what the model predicts, but how its confidence evolves over time and which input features contribute to widening or narrowing the uncertainty bands.

The GRN is the fundamental building block repeated throughout the TFT architecture. It consists of:

• A dense layer with ELU activation for non-linear processing
• A gating mechanism using a sigmoid-activated linear layer that controls information flow
• A residual connection that adds the input directly to the output
• Layer normalization for training stability The gating function is critical for interpretability—it learns to skip unnecessary transformations entirely, providing a binary-like signal of whether a particular sub-network is actively contributing to the prediction. This adaptive depth behavior makes the model's computational graph self-pruning.

TFT uses an LSTM-based encoder-decoder architecture to process known future inputs separately from historical observations. The encoder ingests past time steps and compresses them into hidden states, while the decoder processes known future inputs (like planned promotions or weather forecasts) alongside the encoder's context. This separation is interpretable by design: the attention mechanism operates over the encoder's output, meaning the temporal importance weights explicitly show which historical periods inform the decoder's multi-horizon forecasts. The architecture naturally disentangles past influence from future conditioning.