Paper Citation Record · LEDGER

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 2 inbound Pith citation observations for arXiv:2505.19090.

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

pith.paper-citation-record.v1
2505.19090 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:25:03.949033Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T09:03:49.988075Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:07:39.296153Z

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy18
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc5cec1c-6bee-420f-bcf4-6f68944d1ae3 · outbound

This paper cites write newline.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.792261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3563ec9d-e9db-47ae-b9bf-91b1bf3d7b55 · outbound

This paper cites Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.428956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9cb8825-ffbd-441b-b0ba-f70a9172a7da · outbound

This paper cites an unresolved cited work.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:25:04.413560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 648c458b-2ef1-4b63-9fa1-98018f383fc8 · outbound

This paper cites O., Yoder, N.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations O., Yoder, N

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.396936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 390d97dc-910a-4186-924c-3b81b84838b3 · outbound

This paper cites K., Sen, R., and Yu, R.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations K., Sen, R., and Yu, R

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.380175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 737c0ccb-33c7-43c3-80a8-bba316b0ae16 · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Moment: A family of open time-series foundation models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.363367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd975992-6d89-41a3-83a4-1fe5558625b3 · outbound

This paper cites SOFTS : Efficient multivariate time series forecasting with series-core fusion.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations SOFTS : Efficient multivariate time series forecasting with series-core fusion

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.824727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b61097bb-bdc0-4aa2-bdd9-c082e55d55c8 · outbound

This paper cites Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.335085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 283121a9-4fac-43cf-981b-079eb5a92b0a · outbound

This paper cites A., Jordan, M.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations A., Jordan, M

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.835314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad00b104-14fd-479a-95f8-9aa45c20eeff · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Reversible instance normalization for accurate time-series forecasting against distribution shift

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.840844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f02ad7f-c3ab-4296-825c-4593164e6a33 · outbound

This paper cites SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.845798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f81f64eb-ba65-49d0-8825-a80508ad3387 · outbound

This paper cites Cyclenet: Enhancing time series forecasting through modeling periodic patterns.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Cyclenet: Enhancing time series forecasting through modeling periodic patterns

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.304788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d03ff0b5-0b61-4612-ab5c-d24c99477477 · outbound

This paper cites Sparsetsf: modeling long-term time series forecasting with 1k parameters.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Sparsetsf: modeling long-term time series forecasting with 1k parameters

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.289038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d681122-bd2d-4132-9efb-42eae8dc4ead · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations SCIN et: Time series modeling and forecasting with sample convolution and interaction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.272324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9985583e-bc9b-4b75-8b48-a36ead563433 · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations itransformer: Inverted transformers are effective for time series forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.254434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 691dd8fc-da7c-4d26-8362-1dd8f176cc67 · outbound

This paper cites Timer: Generative pre-trained transformers are large time series models.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Timer: Generative pre-trained transformers are large time series models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.238290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8794cdab-42f5-45cd-a67b-d82d2b760b90 · outbound

This paper cites and Hutter, F.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations and Hutter, F

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.874304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1377dea9-1ab0-4e88-a67c-61c2463c197a · outbound

This paper cites Time series analysis.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Time series analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.209684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ea46ca8-69bb-438e-a76a-6fa189f8e3cd · outbound

This paper cites Nguyen, N., Sinthong, P., and Kalagnanam, J.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Nguyen, N., Sinthong, P., and Kalagnanam, J

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.884195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e746c5b-6e0d-4fa7-8c9f-13b06b32eb41 · outbound

This paper cites N., Carpov, D., Chapados, N., and Bengio, Y.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations N., Carpov, D., Chapados, N., and Bengio, Y

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.182464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a34448d0-7a67-4b91-a4eb-b0182c6dc84c · outbound

This paper cites PyTorch: an imperative style, high-performance deep learning library.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations PyTorch: an imperative style, high-performance deep learning library

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.164952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3984078d-12b1-4ae2-b8ad-4a2a580da989 · outbound

This paper cites S., Sheng, Z., and Yang, B.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations S., Sheng, Z., and Yang, B

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.148223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d152557-71bb-43c4-a4d7-b353b71dcfe9 · outbound

This paper cites Y., and ZHOU, J.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Y., and ZHOU, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.131660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a388f59-b561-4920-b397-8e8e2d393fd0 · outbound

This paper cites Unified training of universal time series forecasting transformers.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Unified training of universal time series forecasting transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.115737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 756bdb41-fe7d-495d-9e99-74800963383d · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.913557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e5d02c6-84cd-4309-952c-13425bea7d8e · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.919145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22ace6ec-c84e-4eaa-9c83-1a10c8c3ab08 · outbound

This paper cites FITS : Modeling time series with \ 10k\ parameters.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations FITS : Modeling time series with \ 10k\ parameters

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.079545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03a945e0-e998-4373-8868-374c02794dad · outbound

This paper cites Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 11121--11128, 2023.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 11121--11128, 2023

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.928257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d63d10b0-8585-4d2e-b9fc-9a7673f46dfa · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.933739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ae29245-7153-4cae-80e0-984f3b55824d · outbound

This paper cites and Yan, J.

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations and Yan, J

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.939527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3081fb82-7f6b-4716-ba19-fdfec7ee141c · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:03.944464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4fc6957-74f0-4189-b922-4b2fbccfa40d · outbound

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

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:25:04.030396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 9ee2aef3-941a-4140-a8dd-e7c450a61d19 · inbound

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

From Observations to States: Latent Time Series Forecasting CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:07:39.299062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5797b787-5bad-42f4-a258-28babd19f3e1 · inbound

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies cites this paper.

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

Reference 109

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:31:26.979336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.