Paper Citation Record · LEDGER

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.03284.

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

pith.paper-citation-record.v1
2501.03284 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:10:19.696291Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

35 of 35 outbound references displayed

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verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cee6fdd-a5cd-4eab-8b48-1c6bc5695eb2 · outbound

This paper cites Attention is all you need.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Attention is all you need

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.421027Z

Source-reported events for the cited work

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Observation 1daf777a-b122-479a-b10c-bfe6eac9580b · outbound

This paper cites The Llama 3 Herd of Models.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting The Llama 3 Herd of Models

Reference 2

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Observation efeb58f0-bb42-4a18-ab17-77571742ac6c · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 3

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Observation 2e31a740-8d58-4992-965a-8a7fc9d7ed5b · outbound

This paper cites Tokens -to-token vit: Training vision transformers from scratch on imagenet.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Tokens -to-token vit: Training vision transformers from scratch on imagenet

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.407954Z

Source-reported events for the cited work

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Observation 4ff8a9fe-0978-4103-95c2-21f22b981205 · outbound

This paper cites Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Reference 5

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Observation 7d973c5b-f241-481f-950e-04673cf38ee6 · outbound

This paper cites Llava-med: Training a large language-and-vision assistant for biomedicine in one day.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Llava-med: Training a large language-and-vision assistant for biomedicine in one day

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.394097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b764f30-2493-493d-8921-38ec58c92ec7 · outbound

This paper cites Are transformers effective for time series forecasting?.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Are transformers effective for time series forecasting?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.373950Z

Source-reported events for the cited work

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Observation b981d77d-e5aa-4bf9-84e9-188b937f7774 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 8

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Observation a070c8e0-b175-4e7a-99a5-205df91c904f · outbound

This paper cites Crossformer: Transformer utilizing cross -dimension dependency for multivariate time series forecasting.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Crossformer: Transformer utilizing cross -dimension dependency for multivariate time series forecasting

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.358408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eca44d66-8798-4a57-8064-f1ee91b343ac · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:19.594337Z

Source-reported events for the cited work

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Observation 678d2865-8fa0-4f15-a065-83bead032b68 · outbound

This paper cites Onenet: Enhancing time series forecasting models under concept drift by online ensembling.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Onenet: Enhancing time series forecasting models under concept drift by online ensembling

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.342720Z

Source-reported events for the cited work

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Observation 59d55cf1-7958-4797-af4a-66f31baf0571 · outbound

This paper cites The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.326312Z

Source-reported events for the cited work

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Observation 7aa82825-a500-43a8-bdd4-bbc788781408 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 13

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Observation 3d2a7f6c-c32a-4483-af36-06c13720a7bf · outbound

This paper cites Scalable Transformer for High Dimensional Multivariate Time Series Forecasting.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Scalable Transformer for High Dimensional Multivariate Time Series Forecasting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.303107Z

Source-reported events for the cited work

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Observation 04948306-f5b7-486f-9d9c-25aeabf3ecd8 · outbound

This paper cites DeepAR: Probabilistic forecasting with autoregressive recurrent networks.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.284629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf7ff202-fbd2-4db1-a252-a22ab39461bc · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Modeling long- and short -term temporal patterns with deep neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.270140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae9ff8cb-695e-4df1-9cb4-81a5b01a43fb · outbound

This paper cites Tensorized LSTM with adaptive shared memory for learning trends in multivariate time series.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Tensorized LSTM with adaptive shared memory for learning trends in multivariate time series

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.251771Z

Source-reported events for the cited work

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Observation f1c65133-82b2-464f-9ffb-d9a6cc354cf6 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 18

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Source-reported events for the cited work

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Observation 2c878797-fe8b-4afb-8137-847a10096050 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Scinet: Time series modeling and forecasting with sample convolution and interaction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.237897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b17f8028-4774-4582-a23a-4b6f461525ef · outbound

This paper cites TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.219668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d5d6278-e528-4f70-8ee5-0da0a0e3fe1b · outbound

This paper cites Multivariate time -series forecasting with temporal polynomial graph neural networks.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Multivariate time -series forecasting with temporal polynomial graph neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.202195Z

Source-reported events for the cited work

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Observation d9ab35de-854a-464c-886b-b2cd6219d18a · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 22

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Observation 057a8058-d2ee-48a7-b210-448a6833f0c6 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 23

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Source-reported events for the cited work

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Observation 5a6962f6-f8c2-4c0f-878b-a8de71bb4a3c · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting A transformer -based framework for multivariate time series representation learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.188999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3fdc9c3c-cd8e-42d8-b812-16fa15874edc · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Informer: Beyond efficient transformer for long sequence time -series forecasting

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.173780Z

Source-reported events for the cited work

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Observation e621ded7-5ec8-4681-a9c5-5ed7d5114084 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.158339Z

Source-reported events for the cited work

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Observation 08c0d8d6-4d8f-4393-a24b-ab2b4abf9dca · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.140619Z

Source-reported events for the cited work

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Observation aa392e57-636f-4169-bb5b-f4baf9b0dbb2 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 28

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Source-reported events for the cited work

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Observation fce7f5a9-77fa-41cf-8b3b-55f5b53874e0 · outbound

This paper cites Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

Reference 29

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Source-reported events for the cited work

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Observation b6d3d1f9-4c9c-484a-8643-e503f985a2c6 · outbound

This paper cites Deep Time Series Models: A Comprehensive Survey and Benchmark.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:19.674626Z

Source-reported events for the cited work

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Observation 6706a7ae-7787-4163-8919-c9b811c2de82 · outbound

This paper cites Layer Normalization.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Layer Normalization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:19.679416Z

Source-reported events for the cited work

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Observation a06bf203-0e48-4a87-a124-094d0e966d81 · outbound

This paper cites Time-Series-Library: A Library for Advanced Deep Time Series Models.

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Time-Series-Library: A Library for Advanced Deep Time Series Models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:20.103626Z

Source-reported events for the cited work

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Observation 3030c8fe-e9b5-40b9-838c-f8518ded2f39 · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 33

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Observation 61a5c8ea-f65f-46da-a3a1-b86b39fdcc6f · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:19.692329Z

Source-reported events for the cited work

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Observation 04765c80-535d-406b-b9ac-3590653690cc · outbound

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

Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting Non- stationary transformers: Exploring the stationarity in time series forecasting

Reference 35

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:10:20.083350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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