FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting | Paper Citation Record

Observation a69adb54-692c-40fe-b5f5-60243d61cd9d · outbound

This paper cites an unresolved cited work.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:09.205373Z

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Observation 9d0ba520-d04c-4eb4-80e6-ff4648e6f092 · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Recurrent neural networks for multivariate time series with missing values

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.193781Z

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Observation d97e5daf-f306-408b-92b5-ef97cad7a46a · outbound

This paper cites The M4 Competition: 100,000 time series and 61 methods.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting The M4 Competition: 100,000 time series and 61 methods

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.182835Z

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Observation 85ac441e-0ae5-40eb-84f7-b5fce4ff6733 · outbound

This paper cites an unresolved cited work.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Unresolved cited work

Reference 4

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Observation 0b801126-e4b9-421d-b548-9ef044714aa0 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Efficient Estimation of Word Representations in Vector Space

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.162108Z

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Observation 8a6c4fb9-ed12-464d-b86b-6ac7fd03cc15 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.International Conference on Learning Representations (ICLR), 2021.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.International Conference on Learning Representations (ICLR), 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.152503Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ef0566b-efe5-4c64-9a50-69f035bb5208 · outbound

This paper cites Are transformers effective for time series forecasting? AAAI Conference on Artificial Intelligence, 2023.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Are transformers effective for time series forecasting? AAAI Conference on Artificial Intelligence, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.141713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e43f6bf8-bf32-408f-849b-36b2fd3fea51 · outbound

This paper cites SAMformer: Unlocking the potential of transformers in time series forecasting with sharpness-aware minimization and channel-wise attention.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting SAMformer: Unlocking the potential of transformers in time series forecasting with sharpness-aware minimization and channel-wise attention

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.131173Z

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Observation c28548d4-3e89-4326-b335-f440112107a0 · outbound

This paper cites ST-ReP: Learning predictive representations efficiently for spatial- temporal forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting ST-ReP: Learning predictive representations efficiently for spatial- temporal forecasting

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.120249Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0990bd79-5411-48fc-954a-870bd1446212 · outbound

This paper cites Long-term forecasting with TiDE: Time-series dense encoder.Transactions on Machine Learning Research, 2023.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Long-term forecasting with TiDE: Time-series dense encoder.Transactions on Machine Learning Research, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.110544Z

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Observation b1c34790-f643-4223-887c-ab9a4ee4be14 · outbound

This paper cites Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Nguyen, Phanwadee Sinthong, Jayant Kalagnanam

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.101423Z

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Observation 5b82b695-1f2f-4290-b49d-05520e761a55 · outbound

This paper cites Quantum Optimal Control of Nuclear Spin Qudecimals in $^{87}\text{Sr}$.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Quantum Optimal Control of Nuclear Spin Qudecimals in $^{87}\text{Sr}$

Reference 12

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Observation ef41d799-8332-468c-ad25-703262e90527 · outbound

This paper cites ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.092221Z

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Observation fee1f950-3e8e-437e-b814-676d6e863e49 · outbound

This paper cites Arik, Tomas Pfister.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Arik, Tomas Pfister

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.082696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc4c474d-406b-4138-ac29-cffd43b72bc2 · outbound

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

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.073614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94fb6e9b-a4ac-4e35-9cf6-8c6d93600999 · outbound

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

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.064518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30016afe-2d4d-41e6-850d-a8d934eb7d01 · outbound

This paper cites Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multi- variate Time Series Forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multi- variate Time Series Forecasting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.054730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2eba1d1a-c06f-489d-aa4b-6fedb8f82638 · outbound

This paper cites STAEformer: Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting STAEformer: Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.044956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8b85701-c861-4b5d-80ce-2a2f3b7e992c · outbound

This paper cites Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.036111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db6282c7-00e7-4f06-8b7a-11691fec2d94 · outbound

This paper cites CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement37th Conference on Neural Information Processing Systems, 2021.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement37th Conference on Neural Information Processing Systems, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.026846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aab78c87-21e5-4664-a4b1-b8f60d2fec85 · outbound

This paper cites Adversarial sparse trans- former for time series forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Adversarial sparse trans- former for time series forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.016937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c2ddcc28-842c-4777-86ce-aff9911bd43b · outbound

This paper cites Factorization machines.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Factorization machines

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:09.007782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dbc849ce-a04c-4720-9cde-1951af696004 · outbound

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

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.998616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7af6627e-287c-4d2f-8645-c47d194a5357 · outbound

This paper cites Liu, Schahram Dustdar.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Liu, Schahram Dustdar

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.989632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 565aa27a-5ae5-48ed-abfb-b1651f62c96d · outbound

This paper cites TimesNet: Temporal 2D- variation modeling for general time series analysis.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting TimesNet: Temporal 2D- variation modeling for general time series analysis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.980770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8eb5d345-43f5-47da-bea1-744729d4f5ac · outbound

This paper cites DeformTime: Capturing variable dependencies with deformable attention for time series forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting DeformTime: Capturing variable dependencies with deformable attention for time series forecasting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.971174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e04aac1-aacc-44e9-bf43-df527b38d37f · outbound

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

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:08.568202Z

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Observation 804907e6-96d3-49b1-8728-a6b1ddfd0ec3 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Linformer: Self-Attention with Linear Complexity

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:08.644737Z

Source-reported events for the cited work

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Observation d850b65d-6f6d-4e3d-83c8-70d30769f047 · outbound

This paper cites Rethinking attention with performers.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Rethinking attention with performers

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.961073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fbbaecba-9713-4a86-bbfa-ad9084d296f2 · outbound

This paper cites Flowformer: Linearizing transformers with conservation flows.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Flowformer: Linearizing transformers with conservation flows

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.951409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39a34d29-4833-4f3b-ad44-078a73959df4 · outbound

This paper cites DeepFM: A factorization-machine based neural network for CTR prediction.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting DeepFM: A factorization-machine based neural network for CTR prediction

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.942350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8cbfd822-334c-4f32-82a9-8252d7797164 · outbound

This paper cites xDeepFM: Combining explicit and implicit feature interactions for recommender systems.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting xDeepFM: Combining explicit and implicit feature interactions for recommender systems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.933037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c95ccab-d0cc-4cc8-834a-06549fd1b52a · outbound

This paper cites Attentional factorization machines: Learning the weight of feature interactions via attention networks.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Attentional factorization machines: Learning the weight of feature interactions via attention networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.923543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b69f229-9172-4b26-b5ba-bb1a23a6cf84 · outbound

This paper cites Deep & cross network for ad click predictions.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Deep & cross network for ad click predictions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.914314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7947b3bb-b289-4f1c-9714-c8cafb4d1aba · outbound

This paper cites Reversible Instance Normalization for Accurate Time-Series Forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Reversible Instance Normalization for Accurate Time-Series Forecasting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.905326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 896f405d-46a2-42c0-b8d1-fc60cd9c6d19 · outbound

This paper cites Gomez, Lukasz Kaiser, Illia Polosukhin.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Gomez, Lukasz Kaiser, Illia Polosukhin

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.896010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1a95bc6-451f-4421-8c70-697b70fd5c59 · outbound

This paper cites CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.886311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dac9e2c9-7850-42f6-8368-436b7dc08d01 · outbound

This paper cites Moment: A family of open time-series foundation models.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Moment: A family of open time-series foundation models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.875652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c03e5c5a-5204-4ce7-a342-665293203211 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:08.685820Z

Source-reported events for the cited work

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Observation 8fd55145-38be-49af-94b0-64e7fc63bd8a · outbound

This paper cites Thomas and Thomas M.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Thomas and Thomas M

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.865760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a14a2373-5d89-4b0c-b541-c2730826fd4d · outbound

This paper cites Forecasting: Principles and Practice.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Forecasting: Principles and Practice

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.856103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e48353c-30d3-4018-9e06-ef9637fd499e · outbound

This paper cites an unresolved cited work.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:21:08.845406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 021bfae2-37c6-46e8-8bcf-9f2348e0c1b7 · outbound

This paper cites Hanssens , Leonard J.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting Hanssens , Leonard J

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.833135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd7b778d-ab2b-440e-9189-8f7608ee0753 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting,International Conference on Learning Representations (ICLR), 2024.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting,International Conference on Learning Representations (ICLR), 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.821355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 629e8921-d1fb-4886-8a8c-5adc27bebaa1 · outbound

This paper cites SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction, 36th Conference on Neural Information Processing Systems, 2022.

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction, 36th Conference on Neural Information Processing Systems, 2022

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.809859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25fe7006-70a2-4805-9b26-99567c143677 · outbound

This paper cites The PEMS dataset consists of traffic data in California that was introduced in [45].

FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecasting The PEMS dataset consists of traffic data in California that was introduced in [45]

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:21:08.799147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers