Paper Citation Record · LEDGER

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment

As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.13175.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.13175 v1

Coverage vector

measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation bb298253-acf4-4b50-b891-4702f1d49afc · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 1

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This paper cites Multimodal machine learn- ing: A survey and taxonomy.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Multimodal machine learn- ing: A survey and taxonomy

Reference 2

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Observation 867fed0f-f181-4c4d-9020-34477875db11 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment On the Opportunities and Risks of Foundation Models

Reference 3

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This paper cites Language models are few-shot learners.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Language models are few-shot learners

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This paper cites Nhits: Neural hierarchical interpolation for time series fore- casting.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Nhits: Neural hierarchical interpolation for time series fore- casting

Reference 5

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Observation 31ae64c1-419e-46e2-a8c1-229aa1a5f472 · outbound

This paper cites Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms

Reference 6

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 7

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This paper cites Audioclip: Extending clip to image, text and audio.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Audioclip: Extending clip to image, text and audio

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This paper cites Context-alignment: Activating and enhancing llm capabilities in time series.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Context-alignment: Activating and enhancing llm capabilities in time series

Reference 9

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This paper cites Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome

Reference 10

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This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Scaling up visual and vision-language representation learning with noisy text supervision

Reference 11

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This paper cites Empowering Time Series Analysis with Large Language Models: A Survey.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Empowering Time Series Analysis with Large Language Models: A Survey

Reference 12

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This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 13

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Large language models are zero-shot reasoners

Reference 14

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

Reference 15

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This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Temporal fusion transformers for interpretable multi-horizon time series forecasting

Reference 16

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Observation 22399c53-db56-4fd4-ae36-df235a96a507 · outbound

This paper cites Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment

Reference 17

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Observation c7822a7e-701a-47df-a114-ff4482e9af3e · outbound

This paper cites Calf: Aligning llms for time series forecasting via cross-modal fine-tuning.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Calf: Aligning llms for time series forecasting via cross-modal fine-tuning

Reference 18

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Observation 958659ff-cb4b-448e-a6c3-d45d390f7549 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 19

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This paper cites Non-stationary transformers: Exploring the stationarity in time series forecasting.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Non-stationary transformers: Exploring the stationarity in time series forecasting

Reference 20

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This paper cites The m4 competition: Results, findings, conclusion and way forward.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment The m4 competition: Results, findings, conclusion and way forward

Reference 21

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Maximum entropy markov models for information extraction and segmentation

Reference 22

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This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 23

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 24

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting

Reference 25

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment A tutorial on hidden markov models and selected applications in speech recognition

Reference 27

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Learning transferable visual models from natural language supervision

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Improving language understanding by generative pre-training

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Language models are unsupervised multitask learners

Reference 30

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 31

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Cross-modal fine-tuning: Align then refine

Reference 32

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This paper cites TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Semantic reconstruction of continuous language from non-invasive brain recordings

Reference 34

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment LLaMA: Open and Efficient Foundation Language Models

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Multimodal transformer for unaligned multimodal language sequences

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

Reference 37

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This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 38

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting

Reference 39

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 40

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This paper cites Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023

Reference 41

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This paper cites A transformer-based framework for multivariate time series representation learning.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment A transformer-based framework for multivariate time series representation learning

Reference 42

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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 43

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This paper cites One fits all: Power general time series analysis by pretrained lm.

Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment One fits all: Power general time series analysis by pretrained lm

Reference 44

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Pith citing papers

No inbound Pith citation observations are available.