Paper Citation Record · LEDGER

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.06607.

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

pith.paper-citation-record.v1
2607.06607 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T01:59:13.445296Z

measured 22 of 22 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 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

22 of 22 outbound references displayed

verified exact3
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcea2695-dc14-4d67-91b3-a45c49f674c9 · outbound

This paper cites Time series models for internet traf- fic.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Time series models for internet traf- fic

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.007005Z

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 35a4b4bb-0c8f-4263-85fb-fbb2ae5589df · outbound

This paper cites Applying time series to power flow analysis in networks with high wind penetration.IEEE transactions on power systems, 22(3):951–957,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Applying time series to power flow analysis in networks with high wind penetration.IEEE transactions on power systems, 22(3):951–957,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.097127Z

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 cd0218cd-b57c-4bfc-9ebc-4dd9fed81162 · outbound

This paper cites Stl: A seasonal-trend decomposition.J.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Stl: A seasonal-trend decomposition.J

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.817695Z

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 35d99caf-6823-4145-b440-cc305648bf04 · outbound

This paper cites Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.852148Z

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 49b5f268-24e6-41ae-beb5-db6fe4191232 · outbound

This paper cites Units: A unified multi-task time series model.Advances in Neural Information Processing Sys- tems, 37:140589–140631,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Units: A unified multi-task time series model.Advances in Neural Information Processing Sys- tems, 37:140589–140631,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.894833Z

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 72116fd2-4d7c-4570-897c-fd65b94a7eff · outbound

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

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Re- versible instance normalization for accurate time-series forecasting against distribution shift

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.977738Z

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 e1c7eabf-92ff-40af-a3e7-ba7d7fdfc1aa · outbound

This paper cites Cesnet-timeseries24: Time series dataset for network traffic anomaly detection and forecast- ing.Scientific Data, 12(1):338,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Cesnet-timeseries24: Time series dataset for network traffic anomaly detection and forecast- ing.Scientific Data, 12(1):338,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.950498Z

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 0036231d-fd16-4c46-888c-e3ef9eb5ad8f · outbound

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

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.785410Z

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 4b2b333d-7494-4f98-9d5a-260dc2562b17 · outbound

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

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 2026-08-10T06:31:04.303077+00:00.

Observation 1bf7d426-4bee-4a67-8646-f5e7fc8cf388 · outbound

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

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-11T02:07:43.959431Z

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 f620d686-c9dd-445d-98e3-1b50bd225386 · outbound

This paper cites Adaptive normalization: A novel data normalization approach for non-stationary time series.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Adaptive normalization: A novel data normalization approach for non-stationary time series

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.067038Z

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 8e9ba65b-8db5-4234-810b-4398bf816010 · outbound

This paper cites Deep adaptive input normalization for time series forecasting.IEEE transactions on neural networks and learning systems, 31(9):3760–3765,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Deep adaptive input normalization for time series forecasting.IEEE transactions on neural networks and learning systems, 31(9):3760–3765,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.924067Z

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 4af21ebb-7433-42e4-9559-fcc7733beb5a · outbound

This paper cites Space weather: Terres- trial perspective.Living Reviews in Solar Physics, 4(1):1,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Space weather: Terres- trial perspective.Living Reviews in Solar Physics, 4(1):1,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:47.924613Z

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 f61ad2fc-a54d-4abd-b436-37266a921ede · outbound

This paper cites A comparison of arima and lstm in forecasting time series.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts A comparison of arima and lstm in forecasting time series

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.037287Z

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 247a81f3-8638-40a3-9aa3-d57325479042 · outbound

This paper cites A practical guide to wavelet analy- sis.Bulletin of the American Meteorological society, 79(1):61–78,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts A practical guide to wavelet analy- sis.Bulletin of the American Meteorological society, 79(1):61–78,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.274644Z

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 662e25f9-20ab-46d6-bd2b-29d7df8295b0 · outbound

This paper cites A review of irreg- ular time series data handling with gated recurrent neural networks.Neurocomputing, 441:161–178,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts A review of irreg- ular time series data handling with gated recurrent neural networks.Neurocomputing, 441:161–178,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.212292Z

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 4e9c80e3-8d9a-4751-b417-5147983728a5 · outbound

This paper cites Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-11T02:07:43.923778Z

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 5a39f14d-3c35-4c39-9ddc-06873133f8c6 · outbound

This paper cites Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing.Advances in Neural Information Processing Systems, 34:22419–22430,.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing.Advances in Neural Information Processing Systems, 34:22419–22430,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.243570Z

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 c39ea7a0-dddc-41c5-8991-23791ba766b3 · outbound

This paper cites Frequency adaptive normalization for non-stationary time series forecasting.Advances in Neural Information Processing Systems, 37:31350–31379.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Frequency adaptive normalization for non-stationary time series forecasting.Advances in Neural Information Processing Systems, 37:31350–31379

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.136415Z

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 d047192f-618a-4223-ba0e-42ea3a3077f0 · outbound

This paper cites Are transformers effective for time series fore- casting? InProceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121–11128.

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Are transformers effective for time series fore- casting? InProceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121–11128

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.306547Z

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 c2e68c8c-121e-4457-8b05-bbcd33225cc0 · outbound

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

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.159871Z

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 1813b098-cb7d-4037-b3d6-7f2b1f16af75 · outbound

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

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts Fedformer: Fre- quency enhanced decomposed transformer for long-term series forecasting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-11T02:07:48.184985Z

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

No inbound Pith citation observations are available.