SemTFT: When Semantics Meet Intermittent Demand Forecasting

Abstract

The extreme sparsity, zero inflation, and weak temporal regularity inherent to intermittent demand forecasting make it difficult to use traditional deep learning models. This paper presents a novel framework called SemTFT, a conditional semantic-temporal fusion transformer, which incorporates semantic information in a controlled, data-driven fashion. In contrast to the previous methods, where embeddings are added randomly, our method is based on a framework of semantic consistency that measures how the similarity of embeddings is related to demand behavior. Semantic features are then selectively activated by a gating mechanism when informative, effectively suppressing semantic noise. To explicitly deal with zero-inflated demand distributions, the model is trained with a Tweedie loss function. This experiment on real-world e-commerce data shows that naive semantic integration may actually decrease the performance, whereas the proposed conditional approach enhances forecasting accuracy, attaining a decrease in MASE and RMSSE compared to the baseline and standard TFT models. Noise and covariate shift robustness tests also substantiate the fact that our approach provides stable performance, emphasizing the role of selective semantic use in intermittent demand forecasting.