VQ-PACT: Learning Compositional Temporal Grammars for Text-Controlled Time Series Generation

Text-controlled time series generation aims to synthesize sequences that follow natural-language descriptions while remaining faithful to real data distributions. Existing approaches often couple semantic understanding and sequence modeling within a unified generative representation, binding reusable temporal regularities to observed joint patterns. This limits the faithful realization of textual semantics and makes accurate local-event generation difficult to reconcile with the preservation of other temporal attributes. To address this challenge, we propose VQ-PACT, a text-controlled generation framework that combines disentangled representations of temporal factors with joint generation under semantic constraints. Its temporal grammar supports factor reuse and composition while preserving multiple numerical realizations of the same semantics, improving semantic consistency and the accuracy of local-event realization. Specifically, VQ-PACT first learns temporal primitives for morphological skeletons, rhythms, and local events, and strengthens their representational stability through recombination-based training to form a reusable temporal grammar. It then aligns textual descriptions with the dynamic shapes of these primitives and composes them according to the specified temporal semantics, constructing a complete temporal representation. Finally, selective semantic constraints are integrated with temporal decoding and scale restoration to realize textual requirements in the corresponding factors and their specified scopes. Experiments on twelve real-world datasets, Synth-U, and real report text from Time-MMD demonstrate improved overall distributional fidelity and fine-grained text control, together with generalization to unseen factor combinations under compositional holdout evaluation.