Paper Citation Record · LEDGER

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

As of 4 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 93 inbound Pith citation observations for arXiv:2310.06625.

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

pith.paper-citation-record.v1
2310.06625 v4

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-13T18:54:58.768947Z

measured 121 of 121 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 93 of 93 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:16:26.773470Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T02:07:43.854233Z

Reference resolution

28 of 28 outbound references displayed

verified exact9
verified fuzzy12
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfc910b4-912b-4fc5-b7d0-9cebad9370f4 · outbound

This paper cites Layer Normalization.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Layer Normalization

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-13T18:54:58.812602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 42ff1189-2b17-44d2-b70e-23e0146a47fd · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-13T18:54:58.798205Z

Source-reported events for the cited work

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Observation 28505d0e-462f-4500-a187-2d03e2f8fbc1 · outbound

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

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.785976Z

Source-reported events for the cited work

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Observation 042d53cc-b502-4262-bccd-42f68cb3ef6a · outbound

This paper cites SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.789374Z

Source-reported events for the cited work

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Observation 1a0f2862-9b04-4f02-ac0b-319a78b2fa03 · outbound

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

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.792455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 38220617-cd6e-409b-b3e3-4fc620e66476 · outbound

This paper cites Scaling Laws for Neural Language Models.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Scaling Laws for Neural Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-13T18:54:58.795378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 89b76566-a00c-4320-9922-ca595d16db93 · outbound

This paper cites Reversible instance normalization for accurate time-series forecasting against distribution shift.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Reversible instance normalization for accurate time-series forecasting against distribution shift

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.833458Z

Source-reported events for the cited work

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Observation c5ba5cc1-ecd9-45d9-a7ad-ef43f0e58517 · outbound

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

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.801351Z

Source-reported events for the cited work

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Observation 015d010c-0484-4bfb-93ef-df01a37d1791 · outbound

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

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:02:44.987405Z

Source-reported events for the cited work

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Observation 729c071d-e3e8-40b2-956d-84e0e656700d · outbound

This paper cites Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.810038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b8e0b376-28e8-4661-8b8c-966b48fe81a0 · outbound

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

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Are transformers effective for time series forecasting? AAAI

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.835332Z

Source-reported events for the cited work

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Observation 65a3fb47-4648-4a8f-a9d4-a26631c9532a · outbound

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation 2b471395-75af-4ce7-9991-024cd6acc523 · outbound

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 13

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Observation 4d84a91d-f625-4a06-bf42-9b0251a1cb9f · outbound

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 10434f9a-8ff1-4bfc-9ffd-048371dcbdb7 · outbound

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 15

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Source-reported events for the cited work

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Observation a7f9f9d6-55d0-47d0-ae83-058fa47421e6 · outbound

This paper cites (7) PEMS contains the public traffic network data in California collected by 5-minute windows.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting (7) PEMS contains the public traffic network data in California collected by 5-minute windows

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.843980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d84f3adf-63f7-40c1-9c35-8187241aeeaa · outbound

This paper cites an unresolved cited work.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-05-13T18:54:58.846066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6e8b9041-d9e5-4e2e-8e19-4852c298749f · outbound

This paper cites Require: Input lookback time series X ∈ RT ×N ; input Length T ; predicted length S; variates number N; token dimension D; iTransformer block number L.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Require: Input lookback time series X ∈ RT ×N ; input Length T ; predicted length S; variates number N; token dimension D; iTransformer block number L

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.848100Z

Source-reported events for the cited work

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Observation 23440ef9-1e6b-4d6d-9284-bfc87f8876b3 · outbound

This paper cites Table 5: Robustness of iTransformer performance.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Table 5: Robustness of iTransformer performance

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.814803Z

Source-reported events for the cited work

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Observation 75e39fd7-322c-4457-93d8-ba3d26e9ac29 · outbound

This paper cites an unresolved cited work.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-05-13T18:54:58.817080Z

Source-reported events for the cited work

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Observation 834542bc-d627-48ad-9b3e-730b9108f063 · outbound

This paper cites The results are recorded with the official model configuration and the same batch size.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting The results are recorded with the official model configuration and the same batch size

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.819106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bb855895-c524-4d10-8cd8-34c8ce319861 · outbound

This paper cites All the cases have distinct multivariate correlations in the lookback and forecast window because the dataset exhibits obvious seasonal changes in the daytime and night.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting All the cases have distinct multivariate correlations in the lookback and forecast window because the dataset exhibits obvious seasonal changes in the daytime and night

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.821164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 23dc6add-c438-44e6-8bf2-e68fde23c659 · outbound

This paper cites Among the various models, iTransformer predicts the most precise future series variations and exhibits superior performance.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Among the various models, iTransformer predicts the most precise future series variations and exhibits superior performance

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.823186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 72bb68fe-8cc7-452b-b1f5-6b00a2788446 · outbound

This paper cites Table 7: Full performance comparison between the vanilla Transformer and the proposed iTransformer.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Table 7: Full performance comparison between the vanilla Transformer and the proposed iTransformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.825310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ab629f89-6842-48ce-948e-bb2146f859ff · outbound

This paper cites A regularity and compactness theory for immersed stable minimal hypersurfaces of multiplicity at most 2.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting A regularity and compactness theory for immersed stable minimal hypersurfaces of multiplicity at most 2

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T18:54:58.804046Z

Source-reported events for the cited work

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Observation 374edf97-98fc-4b3d-9be1-442baeb045af · outbound

This paper cites These works strive to reveal the temporal dependency better.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting These works strive to reveal the temporal dependency better

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.827291Z

Source-reported events for the cited work

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Observation 0a1bf793-45eb-4554-b845-81955f18c531 · outbound

This paper cites More advanced linear forecasters focus on structural point-wise model- ing (Oreshkin et al., 2019; Liu et al., 2022a; 2023).

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting More advanced linear forecasters focus on structural point-wise model- ing (Oreshkin et al., 2019; Liu et al., 2022a; 2023)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T18:54:58.829468Z

Source-reported events for the cited work

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Observation 5ea6121b-1052-460e-8620-d02a3d4d5bb3 · outbound

This paper cites The problem can be alleviated by expanding the receptive field.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting The problem can be alleviated by expanding the receptive field

Reference 28

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Source-reported events for the cited work

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Pith citing papers

Observation ce240f3f-2d54-442b-a2a5-6ceca6a466d9 · inbound

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

Deep Time Series Models: A Comprehensive Survey and Benchmark iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:05:51.454946Z

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Observation 209ac9bf-290e-4f23-bdb2-db0fb1f07bdc · inbound

AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model cites this paper.

AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:05:50.316197Z

Source-reported events for the cited work

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Observation b20f4765-2c74-4c8b-8a48-02332cd065e7 · inbound

Titans: Learning to Memorize at Test Time cites this paper.

Titans: Learning to Memorize at Test Time iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 67

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Source-reported events for the cited work

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Observation f1aa9b99-3cbf-483c-aec2-a008c217d129 · inbound

Neural equilibria for long-term prediction of nonlinear conservation laws cites this paper.

Neural equilibria for long-term prediction of nonlinear conservation laws iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-23T05:07:35.033685Z

Source-reported events for the cited work

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Sundial: A Family of Highly Capable Time Series Foundation Models cites this paper.

Sundial: A Family of Highly Capable Time Series Foundation Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

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Observation e31008b7-fb94-4064-a2a3-685f724c40dd · inbound

OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting cites this paper.

OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:02:30.188784Z

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Observation 705ff4db-f8d6-418a-8c4d-46004c9898f1 · inbound

Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation cites this paper.

Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:57:22.957577Z

Source-reported events for the cited work

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Observation d35a5409-bb51-4cc4-9c81-2732198ec99f · inbound

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting cites this paper.

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 8

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Observation 8ad833a8-93ae-49e2-aae9-aa11886ed0ae · inbound

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs cites this paper.

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 39

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Observation 222d0497-9afd-453c-9f40-301dc3de4159 · inbound

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting cites this paper.

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 23

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Observation f89fbceb-883d-4168-8468-8b4796832fc5 · inbound

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting cites this paper.

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 33

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Observation 7272b789-ea72-4f6e-abac-5259602d05b9 · inbound

ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters cites this paper.

ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 8

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Observation 1d790a86-bcbe-41c6-bf02-1cc6b5726f81 · inbound

ALPINE: Closed-Loop Adaptive Privacy Budget Allocation for Mobile Edge Crowdsensing cites this paper.

ALPINE: Closed-Loop Adaptive Privacy Budget Allocation for Mobile Edge Crowdsensing iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-18T06:25:58.774337Z

Source-reported events for the cited work

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Observation e302378a-c1b2-46cb-a1c3-386c629ff3d2 · inbound

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models cites this paper.

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-22T12:36:32.558745Z

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Observation 3031d8b4-bc3c-4c13-943c-1e937560d702 · inbound

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting cites this paper.

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 4

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MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-17T04:39:03.360571Z

Source-reported events for the cited work

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Observation f223b6f6-8b71-4e1c-be82-43013ea0a7aa · inbound

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-21T18:20:29.096215Z

Source-reported events for the cited work

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Observation 65cce554-bf44-4394-85d0-8e5a24683b93 · inbound

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 27

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Observation 84195aa3-6bd4-42c0-8250-b74fdeef03e3 · inbound

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting cites this paper.

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 2018

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Observation 9f84de36-bca0-4bed-850c-1f19602763a8 · inbound

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models cites this paper.

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T10:37:08.095066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bedbcef0-0f5c-4d0d-9df1-0b13fd30f863 · inbound

From Observations to States: Latent Time Series Forecasting cites this paper.

From Observations to States: Latent Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-16T09:07:39.266179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 86511aaa-e419-46d1-9a60-cf33b4fd8b1b · inbound

AROpt: An Optimization Method for Autoregressive Time Series Forecasting cites this paper.

AROpt: An Optimization Method for Autoregressive Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:26.773470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1a64ef5-71d1-4254-a70e-5e62de897383 · inbound

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks cites this paper.

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T05:20:52.258255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a237c2e8-20f0-4465-ad0c-4a641bdfd591 · inbound

CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data cites this paper.

CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22eaa7e5-8981-4031-9bd7-c7ffadc30030 · inbound

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting cites this paper.

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e969c5af-745b-4e0a-9010-ccc285242c28 · inbound

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting cites this paper.

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-21T10:04:06.696699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4dd3b97b-d45e-4cfe-9442-37352a1fddd2 · inbound

Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation cites this paper.

Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6f987af6-fa0c-43c4-a679-d83b91414981 · inbound

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference cites this paper.

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 21716c14-a1c9-473d-a16b-57e8f48ff395 · inbound

Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator cites this paper.

Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b540ccae-3ab8-442b-a790-750c56791adf · inbound

CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction cites this paper.

CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3f09ce66-abc9-4815-9632-479ad5e6078a · inbound

M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention cites this paper.

M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2cc60d52-9a74-4c8a-9981-777b4dd9f988 · inbound

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration cites this paper.

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-15T14:51:08.454903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eec0ca9b-09d1-4be3-b77a-4afcc4cbd873 · inbound

The CTLNet for Shanghai Composite Index Prediction cites this paper.

The CTLNet for Shanghai Composite Index Prediction iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1b04528d-b3d1-4985-93ff-ae4ccd7ebaec · inbound

Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models cites this paper.

Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 21ae5928-d0fd-4303-8887-d1d3f50660dd · inbound

GeoCert: Certified Geometric AI for Reliable Forecasting cites this paper.

GeoCert: Certified Geometric AI for Reliable Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f6669d95-13f5-44ef-bd05-36842783dcd8 · inbound

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework cites this paper.

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c4a55cb2-5f3f-4935-a01c-66422a30ef8d · inbound

Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization cites this paper.

Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6bfd57c5-cd30-4c6b-841b-6514c20bb6b5 · inbound

From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting cites this paper.

From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 987d63c2-b69c-4c36-8c9f-ee5bc4ccfeb0 · inbound

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting cites this paper.

Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c417b2ae-0975-41bd-be65-61a3b65384fd · inbound

CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models cites this paper.

CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f3859c88-57c8-4a9a-8cf1-6caf3172fb4b · inbound

MedMamba: Recasting Mamba for Medical Time Series Classification cites this paper.

MedMamba: Recasting Mamba for Medical Time Series Classification iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 231a3487-d1d3-4c43-9ae9-e4b0d22526af · inbound

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction cites this paper.

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a96470cf-01c4-4266-ae6a-6511bef57120 · inbound

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters cites this paper.

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 285

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 95760609-c7d0-4776-a397-124f3c6fc5a1 · inbound

A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting cites this paper.

A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fbfe947a-ce53-4362-a2a4-409ecfe0f1b5 · inbound

Risk-Aware Safe Throughput Forecasting for Starlink Networks cites this paper.

Risk-Aware Safe Throughput Forecasting for Starlink Networks iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75bb4546-45d0-4652-9b78-fc0e8e086a26 · inbound

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis cites this paper.

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 1

Resolution: malformed identifier; arxiv_id, observed 2026-05-13T18:54:58.848733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 768f29b6-ab7f-4d20-a77f-6e4d2084e52b · inbound

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

What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T02:13:30.395362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5abf68d1-3a0e-4462-b23c-76e7118b798c · inbound

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies cites this paper.

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-15T01:58:29.223049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fb155c2b-ea9d-4235-84b9-57312fb2831e · inbound

How Do Electrocardiogram Models Scale? cites this paper.

How Do Electrocardiogram Models Scale? iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-20T13:48:19.683733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 875013f6-cdcd-4801-99ec-ec991196eefc · inbound

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density cites this paper.

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T14:43:22.421642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f536d2b1-c908-4c06-9e8f-95b6856353d8 · inbound

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting cites this paper.

L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T12:28:16.782704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bf581654-649f-445c-8418-759d0560eca3 · inbound

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data cites this paper.

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:23:16.808828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37194faf-b8e1-4d9d-a73e-71aa6289b65e · inbound

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data cites this paper.

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:55:00.435083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 13ab2a6b-db10-4f55-81c9-f0f2f8300979 · inbound

DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift cites this paper.

DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-05-20T07:58:07.809528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8a153f6-0fec-46f6-a3a6-2d945fb81f2e · inbound

Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics cites this paper.

Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-05-21T07:19:46.965384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bd56f5e2-1338-4b88-93ac-eaaf70fdd6fa · inbound

Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series cites this paper.

Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-22T00:50:51.160922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d996185a-394f-46ea-9d62-e4b015df51f1 · inbound

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs cites this paper.

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:16:12.844174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 199df278-fb5d-414f-b8e3-dd465dbdff83 · inbound

CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification cites this paper.

CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-22T08:31:16.741375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b584f3c-442c-4dd9-a575-29840910526c · inbound

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery cites this paper.

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:44:42.811302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4cbca48b-e528-4aa5-9525-52317d099e2f · inbound

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting cites this paper.

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T05:10:22.007457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6060bdce-6c93-4f54-8541-cd1d648063b6 · inbound

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting cites this paper.

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-06-30T12:34:38.533601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3634ca50-ecae-4b74-952a-284df3036c01 · inbound

Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration cites this paper.

Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-06-29T14:33:30.774115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 172382be-3688-4c10-b7be-28b418692b3e · inbound

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance cites this paper.

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.338839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3f2cb8db-0254-489c-905e-7ac999c90198 · inbound

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting cites this paper.

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-02T13:06:58.946750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 709c8af3-0a84-4b05-8c24-b6623cf8af29 · inbound

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance cites this paper.

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-02T16:17:09.256765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c8c7b523-1f24-4dba-b463-332a245f7d1a · inbound

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting cites this paper.

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T20:57:23.398592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b7c14f02-4ee0-4481-afe8-7a32239c6e50 · inbound

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data cites this paper.

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:47:37.810608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9047aa99-cd13-44c6-8010-0800f286e4a4 · inbound

Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems cites this paper.

Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-06-27T17:21:06.736069Z

Source-reported events for the cited work

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Observation c07d7f72-a184-4c18-b8ce-26ab97db3423 · inbound

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data cites this paper.

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:27:36.239117Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 516bd27f-a437-46ca-b88e-4d8f39cff217 · inbound

VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation cites this paper.

VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:09:41.870681Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9c7ce68f-f952-4e50-9461-a5b69a630057 · inbound

From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs cites this paper.

From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:19:44.547454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 654d010b-f0e0-4cd3-9071-0f8ab6067575 · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 147

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T08:39:42.548858Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Pretrained Time-Series Foundation Models for Financial Return Forecasting cites this paper.

Pretrained Time-Series Foundation Models for Financial Return Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:29:56.327391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 19d7e476-135d-46ba-b5a3-ccd861f78c7e · inbound

How Good Can Linear Models Be for Time-Series Forecasting? cites this paper.

How Good Can Linear Models Be for Time-Series Forecasting? iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-04T13:19:51.297339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 366aebca-157a-4838-9216-2709e549c1c5 · inbound

How Good Can Linear Models Be for Time-Series Forecasting? cites this paper.

How Good Can Linear Models Be for Time-Series Forecasting? iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:34:34.425372Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0dbe990-be65-4ca7-bc8b-183d0049bc56 · inbound

Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis cites this paper.

Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-01T09:55:40.495032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f33c10e1-3f5a-486d-b71c-41b5755418b6 · inbound

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition cites this paper.

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-02T16:47:08.986449Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f86b59a6-cd10-4afa-b6c2-20186adbfc8c · inbound

SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces cites this paper.

SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T17:27:02.115346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd323326-c9bc-43f0-ab4b-bc79fcd00e1d · inbound

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting cites this paper.

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 1764

Resolution: unresolved; no resolver link, observed 2026-08-02T09:05:36.476880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a600e51-13c3-4992-813e-fc5aad17b1a2 · inbound

Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.

Modular Foundation Models for Time-Series Perception in Digital Twins iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 156

Resolution: unresolved; no resolver link, observed 2026-07-12T01:22:51.284207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beb4351e-b875-4dab-9474-a8192deaf71e · inbound

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates cites this paper.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e455c7dd-b192-4276-b124-37562b7baecd · inbound

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates cites this paper.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b2b333d-7494-4f98-9d5a-260dc2562b17 · inbound

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts cites this paper.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-11T02:07:43.885257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d3e4aa96-1857-43e1-afcf-4cd271e639d9 · inbound

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting cites this paper.

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T10:57:05.494444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f7ec3d23-b18c-4286-a689-6cbc97c36d30 · inbound

Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls cites this paper.

Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T00:32:17.073983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166f967a-075b-4023-89db-80083a00fb7b · inbound

Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series cites this paper.

Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T09:36:39.436015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97f4674c-7c6d-4323-97ae-34f0a49c32e2 · inbound

TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling cites this paper.

TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T06:34:48.981765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13b4b69f-7adc-4e05-bc78-dd1d67ca8137 · inbound

OpenMHC: Accelerating the Science of Wearable Foundation Models cites this paper.

OpenMHC: Accelerating the Science of Wearable Foundation Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 109

Resolution: unresolved; no resolver link, observed 2026-08-02T10:05:16.529234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 742e4bdb-ca96-429c-8d34-5ad5e7842306 · inbound

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations cites this paper.

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T22:20:36.553840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05060b27-5129-4086-a2e5-13e989d35703 · inbound

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring cites this paper.

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T15:58:08.349576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffe3995c-6ecb-4a1d-a1f1-97ed4c20070d · inbound

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling cites this paper.

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T12:43:22.116009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 963ae3c3-33d4-4453-9106-658d4b914c49 · inbound

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification cites this paper.

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-30T11:22:20.171359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2599fb45-9037-4472-8eca-58276f65ccfd · inbound

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion cites this paper.

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T06:43:38.900217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.