arXiv AI
Jun 16
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.
Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
arXiv:2606. 19413v1 Announce Type: new Abstract: Multimodal time series forecasting, which pairs numerical sequences with domain-relevant textual reports, promises to inject world knowledge into forecasting pipelines.
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arXiv AI
Jun 29
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
arXiv:2602. 01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals.
arXiv Machine Learning
23h ago
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
arXiv:2602. 21693v2 Announce Type: replace Abstract: Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging rich information inherent in other modalities.
Hugging Face Trending Papers
Jul 27
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.
arXiv AI
Jul 29
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.
arXiv AI
Jun 17
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.
arXiv AI
1d ago
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.
Hugging Face Trending Papers
Aug 12
FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting
Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data.
arXiv Machine Learning
Aug 4
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.
arXiv Machine Learning
Jun 4
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.
arXiv AI
Aug 7
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.
arXiv Machine Learning
2d ago
GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
arXiv:2608. 13741v1 Announce Type: cross Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation.