Paper Citation Record · LEDGER

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 21 inbound Pith citation observations for arXiv:2502.04395.

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

pith.paper-citation-record.v1
2502.04395 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:44:59.060989Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:44:14.849109Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T19:56:10.514464Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0ecf545-b868-4b79-9780-377aa9e32287 · outbound

This paper cites G., Oreshkin, B.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting G., Oreshkin, B

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.572997Z

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Observation 51f9a2dd-98f9-406f-92f7-b21d24da0a36 · outbound

This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2

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Observation 628d0b91-8fb1-4091-8816-ac628b3f2767 · outbound

This paper cites VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 3

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Observation 86227714-6517-4054-8b72-49bd984e1303 · outbound

This paper cites E., and Shah, K.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting E., and Shah, K

Reference 4

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Observation 60127c4d-fcef-4d3b-860e-f77599b2e806 · outbound

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Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Unresolved cited work

Reference 5

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Observation 3adff88a-f47b-45c0-97b9-008a8cc7d43a · outbound

This paper cites M., Alam, M.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting M., Alam, M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.532286Z

Source-reported events for the cited work

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Observation 051a0775-6dc6-4846-8d64-ed2a88ac7de8 · outbound

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

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Scaling up visual and vision-language representation learning with noisy text supervision

Reference 7

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Observation d2c07c01-be99-447a-9d98-c5252552a991 · outbound

This paper cites Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q

Reference 8

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Observation 47cf497f-1f99-4196-8b03-88b782eff185 · outbound

This paper cites and Suykens, J.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting and Suykens, J

Reference 9

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Observation d1440971-b9a1-4fb2-bdca-730686bbf4a4 · outbound

This paper cites Vilt: Vision-and-language transformer without convolution or region supervision.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Vilt: Vision-and-language transformer without convolution or region supervision

Reference 10

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Observation f40a41ed-950f-45cc-ac97-1bdfd1bb0c71 · outbound

This paper cites Reformer: The efficient transformer.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Reformer: The efficient transformer

Reference 11

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Observation 7ba6f2c3-f0c1-487b-b621-1ed4a2c9f184 · outbound

This paper cites Modeling long-and short-term temporal patterns with deep neural networks.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Modeling long-and short-term temporal patterns with deep neural networks

Reference 12

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Observation 11db342f-dd61-44cf-8424-4ad65036037f · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 13

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Observation b58139a2-c71e-4ab6-8ccf-5d5773e54e4f · outbound

This paper cites Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.448915Z

Source-reported events for the cited work

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Observation be07c52b-c008-4b7b-9789-4d6c0906a478 · outbound

This paper cites Forecasting with time series imaging.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Forecasting with time series imaging

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.435475Z

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Observation 51a62824-5bd8-408f-ae26-934e6c16fd18 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Foundation models for time series analysis: A tutorial and survey

Reference 16

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Observation ce4dbff1-bb93-4925-b076-f8d6affa9a3f · outbound

This paper cites Visual Instruction Tuning.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Visual Instruction Tuning

Reference 17

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Observation c142d466-9c90-4669-a088-8ed10c086a3f · outbound

This paper cites Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T00:44:58.973429Z

Source-reported events for the cited work

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Observation cacdc08d-8832-4660-927d-aa26b9d4d4dd · outbound

This paper cites X., and Dustdar, S.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting X., and Dustdar, S

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.412752Z

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Observation 98ad3f7c-f590-4c2f-9018-a59493c84cdd · outbound

This paper cites Unitime: A language-empowered unified model for cross-domain time series forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Unitime: A language-empowered unified model for cross-domain time series forecasting

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.398958Z

Source-reported events for the cited work

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Observation 8a4aa038-870c-4d99-b20d-843779722fa0 · outbound

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

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Non-stationary transformers: Exploring the stationarity in time series forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.383328Z

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Observation 1147191a-7e61-418a-a95c-acd3f8d39a32 · outbound

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

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting The m4 competition: Results, findings, conclusion and way forward

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.370242Z

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Observation 308a01bf-d0ff-415b-bb57-462d29843360 · outbound

This paper cites R., Jain, L., et al.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting R., Jain, L., et al

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.356690Z

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Observation c64612be-9229-4ca8-ab7f-deb44165c75e · outbound

This paper cites H., Sinthong, P., and Kalagnanam, J.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting H., Sinthong, P., and Kalagnanam, J

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.343156Z

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Observation 8420ca8d-f633-4a52-907f-49191b8a33a8 · outbound

This paper cites N., Carpov, D., Chapados, N., and Bengio, Y.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting N., Carpov, D., Chapados, N., and Bengio, Y

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.329783Z

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Observation 5893ad1f-1709-43b5-a042-907063b28b44 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 26

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Observation db2a9419-c105-41c1-87d5-dd688bf92887 · outbound

This paper cites Visual time series forecasting: an image-driven approach.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Visual time series forecasting: an image-driven approach

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.307685Z

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Observation b5577c54-048b-4fb4-bc69-1067e6785a13 · outbound

This paper cites TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

Reference 28

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Observation d73e9a26-5ff5-414f-9ccb-152f8763ee3b · outbound

This paper cites ETSformer: Exponential Smoothing Transformers for Time-series Forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Reference 29

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This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.293731Z

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Observation 64ffb91c-5dbb-4920-b876-715d4e1767df · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.279891Z

Source-reported events for the cited work

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Observation c884b903-4048-4873-a953-9cbbba06a9fe · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.265473Z

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Observation 7b98579c-7849-403c-a485-7ed05a86e7a3 · outbound

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

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 11121--11128, 2023

Reference 33

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This paper cites Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 34

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This paper cites and Huang, M.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting and Huang, M

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.242899Z

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Observation 058ff896-444b-4a61-9cb3-25ac83a5fd35 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.229439Z

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Observation 1cf8a2a9-1851-487a-9a96-465ce0c98d7a · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.215961Z

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Observation 68b89883-75b6-4a0e-8b02-53982db37e7b · outbound

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

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting One fits all: Power general time series analysis by pretrained lm

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:44:59.201541Z

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Observation b1deae0e-8612-4020-94a6-1accc79645a7 · outbound

This paper cites write newline.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting write newline

Reference 39

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Pith citing papers

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From Images to Signals: Are Large Vision Models Useful for Time Series Analysis? cites this paper.

From Images to Signals: Are Large Vision Models Useful for Time Series Analysis? Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 62

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From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 130

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Observation ac147497-18ee-4ddc-9581-83e7aa0bd14e · inbound

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives cites this paper.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 135

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Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 233

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Diffusion Models for Time Series Forecasting: A Survey cites this paper.

Diffusion Models for Time Series Forecasting: A Survey Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 21

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Watermarking Large Language Model-based Time Series Forecasting cites this paper.

Watermarking Large Language Model-based Time Series Forecasting Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 2025

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CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text cites this paper.

CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 12

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Observation 28c3b664-f50e-4946-9d43-58e4964810a4 · inbound

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting cites this paper.

XFMNet: Decoding Cross-Site and Nonstationary Water Patterns via Stepwise Multimodal Fusion for Long-Term Water Quality Forecasting Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 79

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BEDTime: A Unified Benchmark for Automatically Describing Time Series cites this paper.

BEDTime: A Unified Benchmark for Automatically Describing Time Series Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:56:46.011188Z

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Observation ee286e24-a9fc-43e8-9fb3-fed7e492552f · inbound

TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering cites this paper.

TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:11:09.643438Z

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Observation 241f4525-e6bf-4da0-a90c-52dd35754ea0 · inbound

Overcoming the Modality Gap in Context-Aided Forecasting cites this paper.

Overcoming the Modality Gap in Context-Aided Forecasting Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:30:00.182631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fab0107a-3762-4ba3-87eb-a9895a25dfc7 · inbound

Overcoming the Modality Gap in Context-Aided Forecasting cites this paper.

Overcoming the Modality Gap in Context-Aided Forecasting Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:23.598666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1f06a5-3fd4-473b-af86-27ea7c9bdbf6 · inbound

Discrete Prototypical Memories for Federated Time Series Foundation Models cites this paper.

Discrete Prototypical Memories for Federated Time Series Foundation Models Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:51.881363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f41eeb9-1655-4ae5-b905-d6aa66b935e8 · inbound

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning cites this paper.

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:25.228249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 343966be-8b87-495c-a4ff-426d8989ccfe · inbound

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning cites this paper.

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:49:10.274378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ef25e4e-0cfb-486b-badc-a05ba4486863 · inbound

What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions cites this paper.

What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:13:30.332412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c80472e-d300-4fb1-a14a-744a1abfa695 · inbound

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy cites this paper.

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.663585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1d140e72-5d1a-4e0f-bd63-a8064a95bd69 · inbound

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding cites this paper.

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:23:59.922789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b1ef0f6f-54ab-47c2-b895-b650df1c44c2 · inbound

VESTA: Visual Exploration with Statistical Tool Agents cites this paper.

VESTA: Visual Exploration with Statistical Tool Agents Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 59

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:56:10.515880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ada7a79-3d65-4229-98a3-f5b4ce2f0518 · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-13T05:38:27.357469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f00a7f7a-cd43-4b67-9ff7-14a765116739 · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T07:51:14.811916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.