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Paper Citation Record · LEDGER

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding

As of 18 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.12761.

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

pith.paper-citation-record.v1
2505.12761 v3

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:30:19.644500Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54fed9cd-210d-48e7-a926-ee1defe82f6f · outbound

This paper cites write newline.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding write newline

Reference 1

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Observation 186d55ca-d861-46fa-b070-45b3e1cb863c · outbound

This paper cites Time series forecasting of bed occupancy in mental health facilities in india using machine learning.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Time series forecasting of bed occupancy in mental health facilities in india using machine learning

Reference 2

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doi, observed 2026-08-15T20:30:19.679529Z

Source-reported events for the cited work

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Observation 4a6ec550-3983-41a0-bca9-3210ff2f31b5 · outbound

This paper cites Language Models are Few-Shot Learners.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Language Models are Few-Shot Learners

Reference 3

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Observation 761e8eb5-f0cc-4ef1-ba39-0a04e6438379 · outbound

This paper cites End-to-End Object Detection with Transformers.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding End-to-End Object Detection with Transformers

Reference 4

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Observation 9779981c-c28a-47d6-a8a4-acfa7b59315f · outbound

This paper cites E., and Shah, K.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding E., and Shah, K

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a8badc84-a9aa-4b0c-a5db-aab6ab9b85ef · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

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Observation 15c45d23-452b-4b5d-b74b-d338eaa13a6f · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation ce7071f9-aec1-4211-a95d-b16cd6202873 · outbound

This paper cites Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs

Reference 8

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local_arxiv, observed 2026-08-15T20:30:20.002323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation aa433ece-4a9b-4636-8c78-7a6eb3503520 · outbound

This paper cites The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Reference 9

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Observation b2299ca8-373c-41a0-879e-d532f300a255 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Masked Autoencoders Are Scalable Vision Learners

Reference 10

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Observation 0e36c9fc-9985-4893-b354-52b54ecf722e · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 11

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Observation 1e1564c3-def4-48c8-a799-7c9757c5dbfe · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Adam: A Method for Stochastic Optimization

Reference 12

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Observation ed509bb4-1c23-4d95-8e9d-548d67397f82 · outbound

This paper cites UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting

Reference 13

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Observation accabc79-552a-4fc8-85d2-198cef468f47 · outbound

This paper cites X., and Dustdar, S.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding X., and Dustdar, S

Reference 14

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Source-reported events for the cited work

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Observation e7f4337a-ae60-4913-84a5-f0ec4d9ed5eb · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

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Observation 453cd3b8-39e9-498b-bd2d-76e6ddd84c91 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 16

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Observation 56f3c4b0-8d8b-4113-9ba0-549b758a206b · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 17

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Observation 4833ca5e-68af-43b4-a6cc-5eccad8eba52 · outbound

This paper cites A comprehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective, 2025.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding A comprehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective, 2025

Reference 18

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Observation 36b01d60-4bd9-486a-9d19-f741a7fdb7d3 · outbound

This paper cites Language models are unsupervised multitask learners.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Language models are unsupervised multitask learners

Reference 19

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 01d1c370-8fde-46ef-bfd1-fe3860a7818f · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 20

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Observation 1cacee1b-56d8-4cf3-a494-201ea008cf0f · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding LLaMA: Open and Efficient Foundation Language Models

Reference 21

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Observation 56c17cda-38ab-47b8-a370-13b4f71b119a · outbound

This paper cites Forecasting success: Achieving u.s.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Forecasting success: Achieving u.s

Reference 22

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Source-reported events for the cited work

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Observation 6a890d8f-ef44-4cf3-bf6c-aaca079c841e · outbound

This paper cites Attention Is All You Need.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Attention Is All You Need

Reference 23

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Observation 5b5587e3-0a63-4320-8c36-8ccc7d2053bb · outbound

This paper cites Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Reference 24

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Observation 3a83a48c-7715-4c27-b6e6-881c6a999e9e · outbound

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

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 25

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Observation 2cdf8e62-4076-4b87-9e60-1b9c9b9053fd · outbound

This paper cites CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting

Reference 26

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Observation a2ed150b-7044-4358-969a-fb7755f3c980 · outbound

This paper cites Are Transformers Effective for Time Series Forecasting?.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Are Transformers Effective for Time Series Forecasting?

Reference 27

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Observation 4d2f9943-cb15-4bb8-8ca7-545217b41822 · outbound

This paper cites and Yan, J.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding and Yan, J

Reference 28

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Observation b32d429a-9b94-4d8d-b04c-7ac34e8bb3ef · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 29

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Observation 81fea642-6b46-44dc-881d-2c032d318358 · outbound

This paper cites FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 30

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Pith citing papers

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