Paper Citation Record · LEDGER

Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2410.10469.

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

pith.paper-citation-record.v1
2410.10469 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 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 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:06:03.206788Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:28:44.105748Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2a830250-2b06-44ab-b85c-dc77bedaface · inbound

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis cites this paper.

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:45:47.215935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71d0a4db-9dbc-4b85-b3ba-b19ee61a9352 · inbound

Investigating Compositional Reasoning in Time Series Foundation Models cites this paper.

Investigating Compositional Reasoning in Time Series Foundation Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T17:06:03.206788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dafae293-811d-4462-95cc-a096347d2ade · inbound

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting cites this paper.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T18:58:59.929720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3d34eef-efad-4bc1-8e59-0306bf14b959 · inbound

Byte Pair Encoding for Efficient Time Series Forecasting cites this paper.

Byte Pair Encoding for Efficient Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:35.697678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd30588b-e4ef-41d9-a8b5-7f9a0bdabe9e · inbound

Towards a Foundation Model for Communication Systems cites this paper.

Towards a Foundation Model for Communication Systems Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:58.460028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c92a032c-e41c-4e1d-9ca8-3d5e7c2f74fd · inbound

BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models cites this paper.

BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:25.270311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 040bfa19-454b-4fdb-b6fb-96b107967b40 · inbound

Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction cites this paper.

Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:24:22.777689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69c64ddf-9448-4fe3-9d46-800e51e4ac11 · inbound

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers cites this paper.

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:15:59.650731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe0aa447-f075-43b9-a0d7-6cb6ad91fbce · inbound

DIVER-0 : A Fully Channel Equivariant EEG Foundation Model cites this paper.

DIVER-0 : A Fully Channel Equivariant EEG Foundation Model Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:38.073196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe49cce9-2e1e-47c7-ad2e-5b5b8f0f25b3 · inbound

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting cites this paper.

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T22:09:32.600185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-05T22:31:58.347962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18e32b48-e359-4870-8fa2-d9a8b36dc725 · inbound

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning cites this paper.

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:21:24.306986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8294cf43-3324-49ba-b013-c15743694f95 · inbound

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents cites this paper.

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T19:39:05.055302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c66d3d0-316f-40da-a6b0-397c2d37a545 · inbound

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling cites this paper.

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:50:10.967956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation acf0470f-85b1-4d03-ad97-b13ac76530fa · inbound

Discrete Prototypical Memories for Federated Time Series Foundation Models cites this paper.

Discrete Prototypical Memories for Federated Time Series Foundation Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:51.849899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 704087fc-407d-4728-855b-969642c46b6b · inbound

TempusBench: An Evaluation Framework for Time-Series Forecasting cites this paper.

TempusBench: An Evaluation Framework for Time-Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:05.481479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6e560ba-0ea4-4cbf-9657-21b81a1eac7c · inbound

Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity cites this paper.

Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:10:23.261216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa81ecf6-3f7e-459e-8455-1a381b22e002 · inbound

Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications cites this paper.

Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:11:05.621307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 31a28679-1294-42e5-b59e-318e1fd6aac2 · inbound

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning cites this paper.

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:25.264384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 43962f2f-80f1-4313-b69f-49339360d3f5 · inbound

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning cites this paper.

Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:49:10.324955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6fae1e6-7a8e-4e04-b492-5c04ecb36eab · inbound

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration cites this paper.

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:51:28.877593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5f69a5b8-276b-4133-b014-274f4b54735e · inbound

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models cites this paper.

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:33:43.263686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a79fac86-8619-45fa-958d-26d156672f06 · inbound

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

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:38.530881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52e3034c-bbde-4165-b3b1-25cedf6b8098 · inbound

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models cites this paper.

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:56:34.998558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1ceef60f-c488-4aef-b38d-9d18a1fa17f3 · inbound

TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models cites this paper.

TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:47:59.788457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b865c7ae-2983-4863-99be-cc585c126e3a · inbound

CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts cites this paper.

CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.513709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e1bf60b3-5f69-4661-ba5f-b29f293c9812 · inbound

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics cites this paper.

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.107280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 37f2d32e-bb80-4992-9a22-4426a607e890 · inbound

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data cites this paper.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T09:50:20.337617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ab68a0-c413-4cf4-b9a1-dae7db081830 · inbound

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting cites this paper.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-01T05:16:39.070107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.