This work proposes SAGE (Seeing and Augmenting with Grounded Encoding), an end-to-end CLIP-based framework that jointly models temporal, cross-variable, textual, and visual information and achieves state-of-the-art accuracy.
Abstract
Time series forecasting models operate on raw numerical sequences, lacking the semantic knowledge that domain experts implicitly leverage, such as the physical meaning of each variable, its statistical behavior, and its temporal dynamics. Recent efforts to bridge this gap fall into two camps. Some rely on large language models at inference time, which is computationally expensive. Others apply uniform textual prompts at the dataset level, ignoring the heterogeneous semantics across individual variates. We propose SAGE (Seeing and Augmenting with Grounded Encoding), an end-to-end CLIP-based framework that jointly models temporal, cross-variable, textual, and visual information. The CLIP text encoder processes frequency-enhanced patches and variable tokens, while gated residual paths inject variable-specific descriptions and statistical descriptors. In parallel, the frozen CLIP vision encoder aligns rendered series with temporal representations through a training-only contrastive objective. This dual use of CLIP adds complementary semantic and visual supervision without placing an LLM in the forecasting loop. Across eight long-term benchmarks and M4, SAGE achieves state-of-the-art accuracy. Ablations confirm complementary gains from multimodal alignment and variable-level knowledge.
This work defines cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions and employs low-entropy regularization alongside global cross-modal consistency constraints to regulate the selection behavior.
Xue-Yun Luo, Qiang Lu, Sangui Jian et al.· Proceedings of the Thirty-Fi...· 0 citations
Time series forecasting (TSF) is critical in numerous real-world applications, yet its sequential scalar presentation limits semantic richness and the capture of complex temporal patterns. Recent advances leveraging patchwise modeling and pretrained large language models (LLMs) have achieved notable progress. However, existing methods largely focus on raw sequential patterns while overlooking intrapatch semantics, which limits the richness of time series representations, and they fail to effectively exploit complementary information from multiple views. To tackle these challenges, we propose a self-driven multiview architecture for TSF (SMArT). SMArT enriches intrapatch semantics through self-supervised multiview fusion. It jointly captures fine-grained temporal 1-D dependencies via pointwise self-attention and global temporal 2-D structures via Gramian angular fields (GAFs), all without requiring external supervision. To further bridge the gap between time series data and LLMs, SMArT pioneers a dual-prompt strategy, combining static, dataset-level guidance with dynamic, input-specific prompts derived from frequency-domain decomposition, significantly enhancing LLMs' adaptability and generalization. Extensive experiments across diverse TSF tasks validate SMArT's robustness, achieving state-of-the-art performance in long-term forecasting and excelling in few-shot and zero-shot scenarios. By integrating self-driven multiview learning with LLMs' reasoning power, SMArT establishes an effective framework for TSF. Our code and Supplementary Materials are available at https://github.com/BMRETURN/SMArT.
Wen-Bin Xing, Meng-Ran Li, Bo-Yu Zhang et al.· IEEE Transactions on Neural...· 0 citations
We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, with the goal of understanding which design choices drive effective cross-modal transfer. While language models operate on discrete textual tokens, time series consist of continuous numerical observations with temporal dependencies. To bridge this modality gap, we project fixed-length time-series patches directly into the embedding space of a pretrained GPT-2 backbone, bypassing textual tokenization and treating the Transformer as a generic sequence encoder. Through controlled ablation studies on seven benchmark datasets spanning energy, weather, traffic, and finance, we analyze the effects of (i)~representation strategy (continuous embeddings versus textual serialisation), (ii)~adaptation regime (frozen backbone versus partial or full fine-tuning), (iii)~architectural components such as adapters, pooling strategies, and prediction heads, and (iv)~input context length. Continuous patch-based embeddings consistently outperform textual prompting and randomly initialised backbones. The adapted pipeline attains MASE within the range of specialised forecasting architectures while updating less than 1\% of total model parameters. Results further indicate that freezing the pretrained backbone and training lightweight projection and adapter modules provides a favourable accuracy--efficiency trade-off with stable behaviour across varying context lengths.
Tamanna S. Kumavat, Georg Brunner, Kyriakos Flouris· 0 citations
It is concluded that, on this benchmark and within this family of frozen-encoder architectures, text content is not the operative signal behind the reported gains and the perturbation protocol and evaluation harness are released as a reusable diagnostic toolkit.
K. Sridhar, Atharva Gupta, Nishant Pradhan et al.· 0 citations
REATS is proposed, which leverages LLM reasoning capabilities as an intelligent ensemble router that jointly processes textual temporal pattern descriptions and numerical features to produce interpretable, sample-adaptive ensemble weights through chain-of-thought reasoning.
ConceptTS is introduced, an interpretable forecasting framework that organizes its predictions around named, human-readable concepts that achieves accuracy competitive with strong black-box baselines while producing semantically meaningful concept activations.
