Paper Citation Record · LEDGER

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

As of 15 August 2026, this Paper Citation Record lists 100 of 138 outbound references and 3 inbound Pith citation observations for arXiv:2506.24124.

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

pith.paper-citation-record.v1
2506.24124 v2

Coverage vector

measured 100 of 138 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:29:05.314870Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T01:29:58.474810Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 138 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 12aad24f-4c1e-406c-b27d-1b7eb409f167 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Chronos: Learning the Language of Time Series

Reference 1

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Observation e4672016-a5af-41c7-88cd-de48d2772c31 · outbound

This paper cites Layer Normalization.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Layer Normalization

Reference 2

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Observation ff9ee70f-bbe6-4161-929c-375b1165a6cf · outbound

This paper cites Privacy preserving generative feature transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Privacy preserving generative feature transformation

Reference 3

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Observation b2d385e1-2244-432c-b160-6c549fbb885a · outbound

This paper cites Gorec: a generative cold-start recommendation framework.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Gorec: a generative cold-start recommendation framework

Reference 4

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Observation ad68d57d-d7fb-4549-8bea-0376225aa81f · outbound

This paper cites Multimodality invariant learning for multimedia-based new item recommendation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Multimodality invariant learning for multimedia-based new item recommendation

Reference 5

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Observation 0889fec0-b78d-4e49-a1a1-f61225b3fb93 · outbound

This paper cites Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage

Reference 6

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Observation 3ea3dc0a-3a32-4928-83f7-f4879170aa3c · outbound

This paper cites Deep learning and time series-to-image encoding for finan- cial forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep learning and time series-to-image encoding for finan- cial forecasting

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:28:53.303227Z

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Observation 7620ff6f-c6f1-484d-b88d-a3495ed39952 · outbound

This paper cites Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation

Reference 8

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Observation cb1d611f-4fea-4843-b258-3e9ee3ccafad · outbound

This paper cites Control charts in financial applications: An overview.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Control charts in financial applications: An overview

Reference 9

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Observation a5f2d7b9-7b50-4880-bcd4-d23756c7c686 · outbound

This paper cites Language models are few-shot learners.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Language models are few-shot learners

Reference 10

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Observation 48333e52-ec0f-4a8a-9809-899cd7f2d88f · outbound

This paper cites Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases

Reference 11

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Observation c9cbc451-dea2-4878-a69d-1f5034779d70 · outbound

This paper cites From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals

Reference 12

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Observation 3aea84ee-749e-4558-82b7-d8de20d84d55 · outbound

This paper cites Lightts: Lightweight time series classification with adaptive ensemble distillation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Lightts: Lightweight time series classification with adaptive ensemble distillation

Reference 13

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Observation 2ce06a8f-22ac-4381-9d57-6b96aaa98ed3 · outbound

This paper cites LocVTP: Video-Text Pre-training for Temporal Localization.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LocVTP: Video-Text Pre-training for Temporal Localization

Reference 14

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Observation fa67aee7-565e-495a-9fe2-7ae42c9ee75e · outbound

This paper cites Nhits: Neural hierarchical interpolation for time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Nhits: Neural hierarchical interpolation for time series forecasting

Reference 15

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Observation 66be7787-6e16-469f-84fd-7187b163156b · outbound

This paper cites Multi- model approach for stock price prediction and trading recommendations.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Multi- model approach for stock price prediction and trading recommendations

Reference 16

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Observation f4199341-2d8e-45ab-b958-1587667f075a · outbound

This paper cites Financial time series forecasting with multi-modality graph neural network.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Financial time series forecasting with multi-modality graph neural network

Reference 17

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Observation 965b7b11-eece-4c84-b22e-5c464ee84d34 · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 18

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Observation 1498ee0f-abdc-460f-b88e-4a487efcc00d · outbound

This paper cites TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling

Reference 19

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Observation 6605f5ff-d14f-44bc-a0d4-b2498d82caf4 · outbound

This paper cites Weakly supervised video representation learning with unaligned text for sequential videos.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Weakly supervised video representation learning with unaligned text for sequential videos

Reference 20

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

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This paper cites LANISTR: Multimodal Learning from Structured and Unstructured Data.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LANISTR: Multimodal Learning from Structured and Unstructured Data

Reference 22

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Observation f8e67e57-8267-40b8-941d-9cf3c8b00189 · outbound

This paper cites Unsupervised scalable repre- sentation learning for multivariate time series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unsupervised scalable repre- sentation learning for multivariate time series

Reference 23

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

Reference 24

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Observation 2402a515-8451-4756-a82f-890d49f7306e · outbound

This paper cites Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation

Reference 25

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This paper cites Evolutionary large language model for automated feature transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Evolutionary large language model for automated feature transformation

Reference 26

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This paper cites Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming

Reference 27

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This paper cites Neuro-symbolic embedding for short and effective feature selection via autoregressive generation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Neuro-symbolic embedding for short and effective feature selection via autoregressive generation

Reference 28

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This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives MOMENT: A Family of Open Time-series Foundation Models

Reference 29

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This paper cites Large language models are zero-shot time series forecasters.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Large language models are zero-shot time series forecasters

Reference 30

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Efficiently Modeling Long Sequences with Structured State Spaces

Reference 31

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Audioclip: Extending clip to image, text and audio

Reference 32

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Temporal alignment networks for long- term video

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 34

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep residual learning for image recognition

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Double correction framework for denoising recommendation

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long short-term memory

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Transrac: Encoding multi-scale temporal correlation with transformers for repetitive action counting

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting

Reference 39

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Gpt4mts: Prompt-based large language model for multimodal time-series forecasting

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 42

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Position: What can large language models tell us about time series analysis

Reference 43

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 45

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Reformer: The Efficient Transformer

Reference 46

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

Reference 47

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sehf: A summary- enhanced hierarchical framework for financial report sentiment analysis

Reference 48

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sade: A speaker- aware dual encoding model based on diagbert for medical triage and pre-diagnosis

Reference 49

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Frozen language model helps ecg zero-shot learning

Reference 50

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models

Reference 51

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives VideoChat: Chat-Centric Video Understanding

Reference 52

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Clip-event: Connecting text and images with event structures

Reference 53

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

Reference 54

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep learning models for time series forecasting: a review

Reference 55

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This paper cites Forecasting with time series imaging.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Forecasting with time series imaging

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This paper cites Time series as images: Vision transformer for irregularly sampled time series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time series as images: Vision transformer for irregularly sampled time series

Reference 57

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This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 58

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

Reference 59

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pth and the regulation of mesenchymal cells within the bone marrow niche

Reference 60

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This paper cites Visual instruction tuning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Visual instruction tuning

Reference 61

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Reference 62

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This paper cites Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression

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This paper cites Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling

Reference 64

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This paper cites Scinet: Time series modeling and forecasting with sample convolution and interaction.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Scinet: Time series modeling and forecasting with sample convolution and interaction

Reference 65

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This paper cites Focal: Contrastive learning for multimodal time- series sensing signals in factorized orthogonal latent space.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Focal: Contrastive learning for multimodal time- series sensing signals in factorized orthogonal latent space

Reference 66

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This paper cites Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting

Reference 67

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unitime: A language-empowered unified model for cross-domain time series forecasting

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:00.904259Z

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Non-stationary transformers: Ex- ploring the stationarity in time series forecasting

Reference 69

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Observation be74caf6-ba53-476d-8b93-c2718966e95a · outbound

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.134359Z

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Observation aacc89a5-f6e3-4e5e-adac-90cc3a3a59ec · outbound

This paper cites AutoTimes: Autoregressive Time Series Forecasters via Large Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.280307Z

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Observation 78bc09ea-079c-4151-883d-5eca9fe8ee5a · outbound

This paper cites Timer: Generative pre-trained transformers are large time series models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Timer: Generative pre-trained transformers are large time series models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.404186Z

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Observation 6315744b-487e-448f-8e59-6245bfa6ae04 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Swin transformer: Hierarchical vision transformer using shifted windows

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.514818Z

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Observation b23abbb3-d2c6-4d74-ac0e-27b48ca99517 · outbound

This paper cites A cnn-bilstm-am method for stock price prediction.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A cnn-bilstm-am method for stock price prediction

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.631306Z

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Observation f575aaf1-cb54-4661-a09f-f84009812730 · outbound

This paper cites Howto100m: Learning a text-video embedding by watching hundred million narrated video clips.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Howto100m: Learning a text-video embedding by watching hundred million narrated video clips

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.715495Z

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Observation 5e4334fc-5e37-4b23-87fa-3f56cd0bbb56 · outbound

This paper cites Expanding language-image pretrained models for general video recognition.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Expanding language-image pretrained models for general video recognition

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.824833Z

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Observation a5c6dda5-0abd-44f3-a870-11a65f1f7a81 · outbound

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:01.973202Z

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Observation e5c409ef-0a7d-4e52-9790-1bf5715c70ae · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Representation Learning with Contrastive Predictive Coding

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.114667Z

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Observation 66d8dbab-91c0-4d5c-af8e-9b3c745ff5cd · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.218628Z

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Observation 2ee961b2-2bc9-4391-af25-348f23a172a0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pytorch: An imperative style, high-performance deep learning library

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.308365Z

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Observation a9d6c929-8b36-4fb5-b390-bda00216289a · outbound

This paper cites Learning transferable visual models from natural language supervision.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Learning transferable visual models from natural language supervision

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.411586Z

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.586917Z

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Observation f8810c08-9755-4108-a7ad-296623248da6 · outbound

This paper cites Automatic diagnosis of the 12-lead ecg using a deep neural network.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Automatic diagnosis of the 12-lead ecg using a deep neural network

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.729299Z

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Observation bc625d19-e4bd-4c4d-8895-a6d879bf533a · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives High-resolution image synthesis with latent diffusion models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:02.918008Z

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Observation 1a3a896a-5155-47dd-9c0e-f3188e70785a · outbound

This paper cites A review of deep learning techniques for forecasting energy use in buildings.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A review of deep learning techniques for forecasting energy use in buildings

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.084368Z

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Observation b31f76a3-7a37-41bb-85ed-3265589db840 · outbound

This paper cites Image- based time series forecasting: A deep convolutional neural network approach.Neural Networks, 157:39–53, 2023.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Image- based time series forecasting: A deep convolutional neural network approach.Neural Networks, 157:39–53, 2023

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.220999Z

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Observation 37aee5d3-dbca-499e-a0be-57664450a828 · outbound

This paper cites Dust: Dual swin transformer for multi- modal video and time-series modeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Dust: Dual swin transformer for multi- modal video and time-series modeling

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.332985Z

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Observation 2e281257-1145-46ff-a2bd-75c1dd0387fb · outbound

This paper cites Learning Video Representations using Contrastive Bidirectional Transformer.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Learning Video Representations using Contrastive Bidirectional Transformer

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.461180Z

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Observation 2dc11e7f-c895-4848-b551-e8f1cec888c3 · outbound

This paper cites Videobert: A joint model for video and language representation learning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Videobert: A joint model for video and language representation learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:29:19.700439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation abda9d6e-5505-4bd9-8d7f-2c4a62896087 · outbound

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.796086Z

Source-reported events for the cited work

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Observation 5c8101c3-3abd-4b70-8500-7ed013e5dc5e · outbound

This paper cites Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:03.936579Z

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Observation 7b2ee3d7-90fb-4978-ac46-bbd58fe0f971 · outbound

This paper cites Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:29:19.417664Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a3147d8f-832b-427e-b3f3-8afbb9bde3d3 · outbound

This paper cites Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:29:19.196750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e831c829-605f-4219-991a-3d502d35445b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LLaMA: Open and Efficient Foundation Language Models

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:04.295397Z

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Observation 167d606e-03eb-457b-952c-9774ae0356d7 · outbound

This paper cites Attention is all you need.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Attention is all you need

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:04.435893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2726999-8d48-4c29-a8c9-2386cdd8ecf8 · outbound

This paper cites Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:04.583770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a86caf-e597-4e13-b649-582afd9a3f95 · outbound

This paper cites Micn: Multi-scale local and global context modeling for long-term series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Micn: Multi-scale local and global context modeling for long-term series forecasting

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:04.698553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e26c3b6-34a2-465d-8a82-efc4eac27530 · outbound

This paper cites Long-short temporal contrastive learning of video transformers.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-short temporal contrastive learning of video transformers

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:29:18.967420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e0caaa7-7186-43ea-a47c-e988f75f6f33 · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives ActionCLIP: A New Paradigm for Video Action Recognition

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T21:29:05.004317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d9357f1-380d-45bb-b419-1bd210e8b1f3 · outbound

This paper cites A hierarchal bert structure for native speaker writing detection.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A hierarchal bert structure for native speaker writing detection

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:29:18.704941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2ec5269b-6731-4f8a-b4c0-58c71406f8cb · outbound

This paper cites Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models

Reference 101

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:29:12.093386Z

Source-reported events for the cited work

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Pith citing papers

Observation afd912c9-50f1-41cc-b294-7df8c4ad6a5e · 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 Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.656941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c954d86-2670-4dce-bdaf-02a390bd3724 · inbound

Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering cites this paper.

Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:09:18.666026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7be0cf6d-ed19-4a25-aa4a-7bcbce33dec8 · inbound

Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA cites this paper.

Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-30T01:34:09.349309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.