Cover Image

TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Fan, Fan, Shiming, Wang, Hua
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
by: Zhang, Fan, et al.
Published: (2026)

Empowering Time Series Forecasting with LLM-Agents
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
by: Zhang, Fan, et al.
Published: (2026)

IdealTSF: Can Non-Ideal Data Contribute to Enhancing the Performance of Time Series Forecasting Models?
by: Wang, Hua, et al.
Published: (2025)

PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
by: Wang, Hua, et al.
Published: (2026)

STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions
by: Fan, Junjie, et al.
Published: (2025)

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
by: Ding, Kuiye, et al.
Published: (2025)

EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis
by: Wang, Hua, et al.
Published: (2026)

DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting
by: Zhu, Mingnan, et al.
Published: (2026)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
by: Ding, Rui, et al.
Published: (2025)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
by: Jin, Ming, et al.
Published: (2023)

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
by: Zhang, Weijia, et al.
Published: (2023)

Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
by: Fan, Wei, et al.
Published: (2024)

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
by: Ding, Kuiye, et al.
Published: (2025)

Ellipsoidal Time Series Forecasting
by: Wang, Qilin
Published: (2025)

iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
by: Liang, Ziran, et al.
Published: (2025)

Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
by: Yao, Yueyang, et al.
Published: (2025)

Time Series Forecastability Measures
by: Wang, Rui, et al.
Published: (2025)

Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
by: Chi, Jinjin, et al.
Published: (2026)

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
by: Liu, Chenxi, et al.
Published: (2024)

T-LLM: Teaching Large Language Models to Forecast Time Series via Temporal Distillation
by: Guo, Suhan, et al.
Published: (2026)

Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecasting
by: Yan, Wenbo, et al.
Published: (2025)

LAST SToP For Modeling Asynchronous Time Series
by: Gupta, Shubham, et al.
Published: (2025)

SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting
by: Huang, Ting-Ji, et al.
Published: (2025)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
by: Wang, Tan, et al.
Published: (2025)

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
by: Wang, Shiyu, et al.
Published: (2024)

CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting
by: Wang, Haoxin, et al.
Published: (2023)

ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence
by: Yang, Luoxiao, et al.
Published: (2024)

CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
by: Zha, Yaohua, et al.
Published: (2026)

A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study
by: Fan, Yongda, et al.
Published: (2026)

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
by: Liu, Zhining, et al.
Published: (2025)

From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting
by: Zhang, Xinyu, et al.
Published: (2025)

FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
by: Chen, Chi-Sheng, et al.
Published: (2026)

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
by: Wang, Yuxuan, et al.
Published: (2024)

Test Time Learning for Time Series Forecasting
by: Christou, Panayiotis, et al.
Published: (2024)

FilterNet: Harnessing Frequency Filters for Time Series Forecasting
by: Yi, Kun, et al.
Published: (2024)

Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
by: Liang, Daojun, et al.
Published: (2026)

Are KANs Effective for Multivariate Time Series Forecasting?
by: Han, Xiao, et al.
Published: (2024)