Paper Citation Record · LEDGER

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.09537.

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

pith.paper-citation-record.v1
2607.09537 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T02:18:39.493876Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

32 of 32 outbound references displayed

verified exact0
verified fuzzy0
unresolved31
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d73b911-58e0-4cd0-a684-94931cc71548 · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e31a200-e84c-4c4b-80aa-bd33236446e3 · outbound

This paper cites Vector autoregression and causality: a theoretical overview and simulation study.Econometric reviews, 13(2):259–285, 1994.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Vector autoregression and causality: a theoretical overview and simulation study.Econometric reviews, 13(2):259–285, 1994

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 259b0300-f740-43c6-8daa-03fbc6d4a4f8 · outbound

This paper cites Springer Science & Business Media, 2008.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Springer Science & Business Media, 2008

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation cb72073e-9599-4992-857a-5a090787f6c0 · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.International journal of forecasting, 36(3): 1181–1191, 2020.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Deepar: Probabilistic forecasting with autoregressive recurrent networks.International journal of forecasting, 36(3): 1181–1191, 2020

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 31784e3d-3605-4aa5-bb06-545d1fb69046 · outbound

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

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Modeling long-and short-term temporal patterns with deep neural networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Unavailable: canonical work link unavailable.

Observation 2a1500bd-b0fa-4773-901b-66536e25b84d · outbound

This paper cites Segrnn: Segment recurrent neural network for long-term time series forecasting.IEEE Internet of Things Journal, 2025.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Segrnn: Segment recurrent neural network for long-term time series forecasting.IEEE Internet of Things Journal, 2025

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2aec5c9-0661-4fcb-a4b7-889114e605b7 · outbound

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

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation af5f3976-aa64-42ab-b578-56858b89e7b5 · outbound

This paper cites Scinet: Time series modeling and forecasting with sample convolution and interaction.Advances in neural information processing systems, 35:5816–5828, 2022.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Scinet: Time series modeling and forecasting with sample convolution and interaction.Advances in neural information processing systems, 35:5816–5828, 2022

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 2060c7ef-4f36-4aa6-a886-e31cf3b2a6a1 · outbound

This paper cites Are transformers effective for time series forecasting? InProceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Are transformers effective for time series forecasting? InProceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation b943447d-557a-4b2c-b4f4-6d7e7d14d229 · outbound

This paper cites Timemixer: Decomposable multiscale mixing for time series forecasting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Timemixer: Decomposable multiscale mixing for time series forecasting

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 2313792c-9bf4-49fe-b114-748d249d6e7f · outbound

This paper cites Bridging past and future: Distribution-aware alignment for time series forecasting.arXiv preprint arXiv:2509.14181, 2025.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Bridging past and future: Distribution-aware alignment for time series forecasting.arXiv preprint arXiv:2509.14181, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 57c29b95-2885-4218-bba1-101fd7c1970f · outbound

This paper cites Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting.Advances in neural information processing systems, 34:22419–22430, 2021.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting.Advances in neural information processing systems, 34:22419–22430, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation a9b978d2-c7da-438a-b3cb-3536a6606e3a · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting A time series is worth 64 words: Long-term forecasting with transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 94cd6a41-5f95-427b-a12b-42b3806b39ba · outbound

This paper cites itransformer: Inverted transformers are effective for time series forecasting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting itransformer: Inverted transformers are effective for time series forecasting

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation eb0d3ff5-55d7-4430-99cc-6d2f8b04d147 · outbound

This paper cites Phaseformer: From patches to phases for efficient and effective time series forecasting.arXiv preprint arXiv:2510.04134, 2025.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Phaseformer: From patches to phases for efficient and effective time series forecasting.arXiv preprint arXiv:2510.04134, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 05bb97e6-8d36-4532-91c6-d60bd8c6a137 · outbound

This paper cites Filternet: Harnessing frequency filters for time series forecasting.Advances in Neural Information Processing Systems, 37:55115–55140, 2024.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Filternet: Harnessing frequency filters for time series forecasting.Advances in Neural Information Processing Systems, 37:55115–55140, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation d3eacbbb-9ad6-4fc9-846a-f9b9948525a1 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation aefcd37f-4c39-436e-b522-c28d0e5839cc · outbound

This paper cites Freqcycle: A multi-scale time-frequency analysis method for time series forecasting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Freqcycle: A multi-scale time-frequency analysis method for time series forecasting

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 69309c39-e00c-4bb7-b986-a45896f652d7 · outbound

This paper cites Temporal query net- work for efficient multivariate time series forecasting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Temporal query net- work for efficient multivariate time series forecasting

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 58ee8397-5087-4f26-a56b-0b9329cc782b · outbound

This paper cites Syntsbench: Rethinking temporal pattern learning in deep learning models for time series.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Syntsbench: Rethinking temporal pattern learning in deep learning models for time series

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Unavailable: canonical work link unavailable.

Observation f60fb733-f9dd-4c65-bd8a-0445aac799a5 · outbound

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

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Unavailable: canonical work link unavailable.

Observation 975e6e6b-848d-4724-bab4-528f71936b41 · outbound

This paper cites Mixlinear: Extreme low resource multivariate time series forecasting with 0.1 k parameters.arXiv preprint arXiv:2410.02081, 2024.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Mixlinear: Extreme low resource multivariate time series forecasting with 0.1 k parameters.arXiv preprint arXiv:2410.02081, 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 1fe724b4-1613-4622-b1c6-4da095295193 · outbound

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

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation f0c2de83-d0ec-4463-8852-869a0aafc132 · outbound

This paper cites Nhits: Neural hierarchical interpolation for time series fore- casting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Nhits: Neural hierarchical interpolation for time series fore- casting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation f0732c9b-5584-4bd8-b9f2-82717ef6f075 · outbound

This paper cites TSMixer: An All-MLP Architecture for Time Series Forecasting.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting TSMixer: An All-MLP Architecture for Time Series Forecasting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 1767e01c-c8a7-477c-ab43-289889d26c6c · outbound

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

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Unavailable: canonical work link unavailable.

Observation 92019c77-5cbf-4574-8089-1c9d2d81b781 · outbound

This paper cites Cyclenet: Enhancing time series forecasting through modeling periodic patterns.Advances in Neural Information Processing Systems, 37:106315–106345, 2024.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Cyclenet: Enhancing time series forecasting through modeling periodic patterns.Advances in Neural Information Processing Systems, 37:106315–106345, 2024

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Unavailable: canonical work link unavailable.

Observation 75882d1d-7228-42a9-b8f8-ec2a0c1ee769 · outbound

This paper cites TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a812366e-ba53-41d2-b25b-169351fff121 · outbound

This paper cites Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation ae79f4d9-1604-4134-9bc2-97f1527cf047 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation c0b48340-4a89-4729-a6dc-f937c20b4f00 · outbound

This paper cites Limitations.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting Limitations

Reference 31

Resolution: malformed identifier; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Observation 54c8e6e5-0bac-4ff6-850d-cad68ef5b419 · outbound

This paper cites IRB or equivalent approval is therefore not applicable to the described study.

GatedLinear: Adaptive Routing of Complementary Linear Bases for Time Series Forecasting IRB or equivalent approval is therefore not applicable to the described study

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-13T02:18:39.493876Z

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Pith citing papers

No inbound Pith citation observations are available.