Discrete events such as holidays, policy announcements, and public gatherings leave no trace in historical time series yet fundamentally shape future demand. Language models capture precisely this event knowledge, making them a natural source of auxiliary signal for event-driven forecasting. However, some multimodal forecasters borrow semantic alignment from vision-language pretraining, enforcing geometric proximity between text and series embeddings. For time series regression, this objective is theoretically unnecessary and actively harmful when training data is limited or text-series coupling is weak: contrastive gradients compress the high-variance series features required to represent event-induced spikes. We propose predictive grounding as the principled alternative, shaping language representations through prediction loss alone. PROSE (Predictive grOunding for event-driven time-Series forEcasting) realizes this framework through a fully fine-tuned language encoder, token-level Event Token Injection, LLM-generated Trajectory Reasoning Anchors as global prefix tokens, and a Soft Functional Alignment loss. On a 464K-sample Chinese holiday benchmark, PROSE surpasses the strongest baseline by 10.7% WMAPE; on Time-MMD, it achieves state-of-the-art results across 7 of 9 domains.

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet. In this work, we introduce THE-X, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks. THE-X proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and LayerNorm. Experiments reveal our proposed THE-X can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage.