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Paper Citation Record · LEDGER

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics

As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2508.11680.

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

pith.paper-citation-record.v1
2508.11680 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:31:58.375657Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

20 of 20 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 878012ec-be27-4dc2-a693-d62361383dca · outbound

This paper cites Chronos: Learning the Language of Time Series.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Chronos: Learning the Language of Time Series

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 335d792f-c6e6-425f-a590-d94e220b42bd · outbound

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

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2

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no resolver link, observed 2026-08-05T22:31:58.322534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8c9fa6c4-d01d-4439-a42b-5209e961cb3c · outbound

This paper cites Time Se- ries Analysis: Forecasting and Control.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Time Se- ries Analysis: Forecasting and Control

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b3108df6-ae67-402e-9061-23eb69400095 · outbound

This paper cites Long-term forecasting with tide: Time-series dense encoder.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Long-term forecasting with tide: Time-series dense encoder

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 53fbe127-f1ec-47db-882a-8081529b67a0 · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics A decoder-only foundation model for time-series forecasting

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9f1a555b-7bbf-4a38-8132-bfa58d44adc2 · outbound

This paper cites Demographic change and urban health: Towards a novel agenda for delivering sus- tainable and healthy cities for all.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Demographic change and urban health: Towards a novel agenda for delivering sus- tainable and healthy cities for all

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation d58a8a52-a65c-46d2-aba5-566773b17c35 · outbound

This paper cites In-context fine- tuning for time-series foundation models.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics In-context fine- tuning for time-series foundation models

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 79a5e223-f226-4d99-a13c-64910c4c15ac · outbound

This paper cites Forecasting small area populations with long short-term mem- ory networks.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Forecasting small area populations with long short-term mem- ory networks

Reference 8

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raw_fallback, observed 2026-08-05T22:31:58.637909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 5e2f7654-e576-410e-aaee-20dfab4abc7c · outbound

This paper cites Foundation Models for Time Series: A Survey.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Foundation Models for Time Series: A Survey

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 49cbc84d-0027-4a79-a9ee-7481fe598ad3 · outbound

This paper cites Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cb3f2198-075c-46d4-ab2f-4ea99bb4d48e · outbound

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

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics A time series is worth 64 words: Long-term forecasting with transformers

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ef4eabd4-9189-4303-9027-6fa57e28d3c8 · outbound

This paper cites N-beats: Neural basis ex- pansion analysis for interpretable time series fore- casting.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics N-beats: Neural basis ex- pansion analysis for interpretable time series fore- casting

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 27c7ed3a-e4d7-4bf8-b1ee-fc5b64ee5a21 · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8e4a7444-46eb-4aa8-9670-ad017d5a2ae4 · outbound

This paper cites Macroeconomics and reality.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Macroeconomics and reality

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 046a36e8-49a4-4fbe-911d-3c51b0ab38a5 · outbound

This paper cites A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

Reference 15

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raw_fallback, observed 2026-08-05T22:31:58.592270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f2ce7cd1-29aa-4aa8-a2b6-183dbfd7bb18 · outbound

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

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a6d5ea31-9c73-492d-827f-7214906e499e · outbound

This paper cites Timesnet: Tem- poral 2d-variation modeling for general time se- ries analysis.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Timesnet: Tem- poral 2d-variation modeling for general time se- ries analysis

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ce865af3-91f4-4c99-8045-dbaee3da1516 · outbound

This paper cites TimeFound: A Foundation Model for Time Series Forecasting.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics TimeFound: A Foundation Model for Time Series Forecasting

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 29a52f87-45cb-45c2-9d1e-6028fa4491e6 · outbound

This paper cites Are transformers effective for time series forecast- ing? Proceedings of the AAAI Conference on Arti- ficial Intelligence , 37:11121–11128, 2023.

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Are transformers effective for time series forecast- ing? Proceedings of the AAAI Conference on Arti- ficial Intelligence , 37:11121–11128, 2023

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 5b77538e-261c-4eec-b30a-0fb9a4542d1e · outbound

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

Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.