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Paper Citation Record · LEDGER

MOMENT: A Family of Open Time-series Foundation Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 58 inbound Pith citation observations for arXiv:2402.03885.

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

pith.paper-citation-record.v1
2402.03885 v3

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measured 0 of 0 reference resolution

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measured 58 of 58 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 58 of 58 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:41:00.281583Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-07-03T17:38:43.284760Z

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Outbound references

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Pith citing papers

Observation 7a53f787-d66a-43ef-8d02-c99618f5e7a6 · inbound

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

Chronos: Learning the Language of Time Series MOMENT: A Family of Open Time-series Foundation Models

Reference 33

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arxiv_id, observed 2026-05-13T08:27:23.418027Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6bfe8d42-dc3c-4021-8f3d-ee8b9666e359 · 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 MOMENT: A Family of Open Time-series Foundation Models

Reference 11

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arxiv_id, observed 2026-05-23T19:45:47.135858Z

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Observation 1970cbde-12ba-4f23-a488-ddac4fe26f9b · inbound

Sundial: A Family of Highly Capable Time Series Foundation Models cites this paper.

Sundial: A Family of Highly Capable Time Series Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 6

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arxiv_id, observed 2026-05-23T04:32:33.879463Z

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Observation 057da507-5440-433c-a435-7733a6d55e9e · inbound

Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer cites this paper.

Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer MOMENT: A Family of Open Time-series Foundation Models

Reference 12

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Observation 3b5aec11-cfd0-4549-b884-8656a9799f14 · inbound

Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting cites this paper.

Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting MOMENT: A Family of Open Time-series Foundation Models

Reference 17

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Observation cfa16717-6656-43d0-91f6-194c9d544c4e · inbound

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions cites this paper.

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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Observation 683ee7c3-0140-4cc6-92d6-e9acce258bf4 · inbound

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models cites this paper.

Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 30

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Observation 2d7ef63f-7286-4b97-8fe0-89003976937e · inbound

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs cites this paper.

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs MOMENT: A Family of Open Time-series Foundation Models

Reference 13

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Observation 4d559046-14b6-46c3-98f3-bfc87ca7a9b1 · inbound

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics cites this paper.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics MOMENT: A Family of Open Time-series Foundation Models

Reference 60

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Observation ff69d990-fea8-4712-a214-5936477b5c6f · inbound

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives cites this paper.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives MOMENT: A Family of Open Time-series Foundation Models

Reference 29

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Observation 71d08308-1006-449f-8cc3-f930f33cc915 · inbound

Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models cites this paper.

Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models MOMENT: A Family of Open Time-series Foundation Models

Reference 49

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arxiv_id, observed 2026-05-22T00:00:48.014730Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b8cf1974-f6b9-458a-990f-da6e2d295c59 · inbound

Grounding Intelligence in Movement cites this paper.

Grounding Intelligence in Movement MOMENT: A Family of Open Time-series Foundation Models

Reference 30

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Observation c3394481-3e8c-491b-bb72-44236b39135c · inbound

Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives? cites this paper.

Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives? MOMENT: A Family of Open Time-series Foundation Models

Reference 11

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Observation 53a730d1-4ce5-4125-ad70-893c314796a8 · inbound

Causal Foundation Models: Disentangling Physics from Instrument Properties cites this paper.

Causal Foundation Models: Disentangling Physics from Instrument Properties MOMENT: A Family of Open Time-series Foundation Models

Reference 4

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Observation 82dfdd99-083d-4a00-b54e-8be3a3892ea4 · inbound

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models cites this paper.

MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models MOMENT: A Family of Open Time-series Foundation Models

Reference 9

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Observation 19c89c08-3dea-4e58-b568-fad49162b233 · inbound

Towards Interpretable Time Series Foundation Models cites this paper.

Towards Interpretable Time Series Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 9

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Observation 4d3697b6-780c-4db5-bd2f-0a729ce250a4 · inbound

Benchmarking Foundation Models with Multimodal Public Electronic Health Records cites this paper.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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Observation 5440a838-ce8c-4ba3-9d98-45f8493b3a76 · inbound

Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models cites this paper.

Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 10

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Observation 27bf8389-e1e6-404b-a284-9ae33396a162 · inbound

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating cites this paper.

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating MOMENT: A Family of Open Time-series Foundation Models

Reference 20

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Observation 949d69ba-61ea-4af9-8db1-f3d7ca6ffd6e · inbound

CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams cites this paper.

CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams MOMENT: A Family of Open Time-series Foundation Models

Reference 6

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Observation 3c7ce6ad-5008-41ff-8485-46ec99e2ac62 · inbound

WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting cites this paper.

WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting MOMENT: A Family of Open Time-series Foundation Models

Reference 21

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Observation 7fe332fe-87a7-4d94-a620-71e76ff24fad · inbound

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting cites this paper.

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting MOMENT: A Family of Open Time-series Foundation Models

Reference 12

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arxiv_id, observed 2026-05-25T08:25:34.179436Z

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Observation af99ebbb-3dce-4f29-b59b-51bc96facb2b · inbound

TS-Arena -- A Live Forecast Pre-Registration Platform cites this paper.

TS-Arena -- A Live Forecast Pre-Registration Platform MOMENT: A Family of Open Time-series Foundation Models

Reference 12

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arxiv_id, observed 2026-05-16T20:01:13.258555Z

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Observation f67d3f26-55f7-4c16-8076-bff2006aa847 · inbound

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition cites this paper.

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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Observation ba755342-3750-4734-8152-8e6098ee0ae9 · inbound

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models cites this paper.

TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models MOMENT: A Family of Open Time-series Foundation Models

Reference 23

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Observation 2adc1764-6968-4d4b-915c-df6be3b6d493 · inbound

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection cites this paper.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection MOMENT: A Family of Open Time-series Foundation Models

Reference 2012

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Observation def22747-0fa9-4d25-bc7c-5769f389ef66 · 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 MOMENT: A Family of Open Time-series Foundation Models

Reference 22

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arxiv_id, observed 2026-05-15T16:50:10.979290Z

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Observation 6a104a3d-4666-4dc2-82f4-78341699c686 · inbound

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

Discrete Prototypical Memories for Federated Time Series Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 10

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Observation 7a9d5627-cd02-4b5a-a983-c0aeead515b7 · inbound

Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model cites this paper.

Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model MOMENT: A Family of Open Time-series Foundation Models

Reference 33

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arxiv_id, observed 2026-05-10T22:20:48.293849Z

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Observation 9a986f3d-5d4e-418f-875b-b62cf4dfc0ad · inbound

LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring cites this paper.

LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring MOMENT: A Family of Open Time-series Foundation Models

Reference 19

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arxiv_id, observed 2026-05-11T00:05:50.918041Z

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Observation 9899991e-64d4-4852-a342-b727d5feff50 · inbound

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale cites this paper.

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale MOMENT: A Family of Open Time-series Foundation Models

Reference 13

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arxiv_id, observed 2026-05-11T10:36:04.449692Z

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Observation 6a1a7912-f311-490c-80d3-70d1714e2655 · inbound

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

TempusBench: An Evaluation Framework for Time-Series Forecasting MOMENT: A Family of Open Time-series Foundation Models

Reference 6

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Observation f01e6195-98e0-4cb3-ba11-684e49b923c9 · inbound

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring cites this paper.

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring MOMENT: A Family of Open Time-series Foundation Models

Reference 13

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arxiv_id, observed 2026-05-11T13:36:09.807943Z

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Observation 7202da64-eade-4a00-935c-284a02e53d05 · inbound

GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation cites this paper.

GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation MOMENT: A Family of Open Time-series Foundation Models

Reference 14

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arxiv_id, observed 2026-05-11T14:31:08.007982Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9a0e9756-0da1-46d9-b963-46ba28318fd7 · 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 MOMENT: A Family of Open Time-series Foundation Models

Reference 6

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arxiv_id, observed 2026-05-11T15:11:05.664195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6259f46f-07de-4fbc-848d-99712e3f11ca · inbound

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning cites this paper.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning MOMENT: A Family of Open Time-series Foundation Models

Reference 13

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arxiv_id, observed 2026-05-11T20:36:09.655524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T08:30:19.662927Z digest=sha256:b1375a5a4f6612cdd1841d35615f15429628640d1cdd988cb9895a93889e77b9

Observation ef02ab16-e459-4132-818f-6a35b788e38c · inbound

GAFSV-Net: A Vision Framework for Online Signature Verification cites this paper.

GAFSV-Net: A Vision Framework for Online Signature Verification MOMENT: A Family of Open Time-series Foundation Models

Reference 5

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verified exact
arxiv_id, observed 2026-05-11T14:46:08.821400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T21:07:33.152991Z digest=sha256:948ae86d966e03a6c3c5ca2d90e59f8ffb055c0681a8d2063cb85c0d8618a6c6

Observation 6682066d-01df-45d2-ba89-2991bf4095ff · inbound

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning cites this paper.

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning MOMENT: A Family of Open Time-series Foundation Models

Reference 49

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arxiv_id, observed 2026-05-12T02:51:17.571623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T02:50:22.377716Z digest=sha256:cbdcf138ada7ea30aca67477ce291f994d48853d4a61578bfbddac0b8d937e54

Observation 4f116a23-9268-4532-9d07-e48874b8d336 · inbound

NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces cites this paper.

NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces MOMENT: A Family of Open Time-series Foundation Models

Reference 24

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arxiv_id, observed 2026-06-30T21:15:04.410200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T21:09:51.002853Z digest=sha256:bfccdbc81a09cdb5452af31b076891a02b29daf19b7c9371f88fabc9956bb2c9

Observation 8b6a347b-05cd-4401-afdd-338f25a01efc · inbound

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density cites this paper.

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density MOMENT: A Family of Open Time-series Foundation Models

Reference 4

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arxiv_id, observed 2026-05-20T14:43:22.391194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T14:42:04.841976Z digest=sha256:43defd25859c4eda3827e0bcbe80a483f3e75adef1fb10928cc1ed95f00f9721

Observation d0694b46-d617-42b0-9e98-b8b5d9e0a7b3 · inbound

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs cites this paper.

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs MOMENT: A Family of Open Time-series Foundation Models

Reference 35

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arxiv_id, observed 2026-05-22T07:16:12.861518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T07:15:45.332957Z digest=sha256:15bc58b61fb45168fbd92bf7b36381a2c47a84af832a1a9f88ae1f58df798d48

Observation 55407af8-1e8e-4176-b1aa-07246de9293e · inbound

AION: Next-Generation Tasks and Practical Harness for Time Series cites this paper.

AION: Next-Generation Tasks and Practical Harness for Time Series MOMENT: A Family of Open Time-series Foundation Models

Reference 9

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arxiv_id, observed 2026-06-30T11:54:38.605864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T11:24:42.704735Z digest=sha256:60186503b9f98aefb723127857558363c01d9865b549d0654cd62dfcbabb77ae

Observation e41681b8-fac3-4a2b-87d1-aa356dfef8fc · inbound

Why Do Time Series Models Need Long Context Windows? cites this paper.

Why Do Time Series Models Need Long Context Windows? MOMENT: A Family of Open Time-series Foundation Models

Reference 16

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verified exact
arxiv_id, observed 2026-07-01T21:56:16.553051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T15:52:40.568646Z digest=sha256:03d307d5dc8adf99b241728646af4ba3cf147e7eb9654022d8c2b487a2fe76da

Observation 46bf2f04-dcb3-4b19-9961-8121ac609b62 · inbound

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification cites this paper.

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification MOMENT: A Family of Open Time-series Foundation Models

Reference 6

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verified exact
arxiv_id, observed 2026-07-02T05:16:39.887625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T08:18:12.182985Z digest=sha256:65bac62e7e25024293690430237514dfe3ceb45d1498d8dee2e09bd129f9a760

Observation f0c3df49-16e5-425b-a820-dfc73ff0ac4d · 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 MOMENT: A Family of Open Time-series Foundation Models

Reference 8

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verified exact
arxiv_id, observed 2026-07-02T03:56:35.001344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T09:32:52.556105Z digest=sha256:3e6820ba62d25b893c35b1121e7911c7516e42f12e02d8e2196c2b744ea881a9

Observation b403e40c-26da-40e1-b7ad-80fdbe3d5809 · inbound

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models cites this paper.

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 7

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verified exact
arxiv_id, observed 2026-07-02T12:46:57.001121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T01:50:24.426751Z digest=sha256:1f39a37f40e3056d584489bbc5e0b6e4bd9ab991a7b6f59177e8ddcb8f4de0f1

Observation ff771bf3-b11c-4d1c-bb38-4a64df1e5cbe · inbound

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data cites this paper.

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data MOMENT: A Family of Open Time-series Foundation Models

Reference 10

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arxiv_id, observed 2026-07-03T04:27:36.225554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T13:58:35.729820Z digest=sha256:d7ce2f57ab0e0399407c1c4ac02d9b8aa7dde5b1112f6b032f7ac4e8ac7bc812

Observation 7357365b-54f2-4ac1-aa76-791a7a8979f7 · inbound

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings cites this paper.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings MOMENT: A Family of Open Time-series Foundation Models

Reference 1

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verified exact
arxiv_id, observed 2026-06-29T19:13:52.903050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T04:54:39.686908Z digest=sha256:b3816329cb3ddcc545717d85534cb976a77f0c08d72399b14f346b4d8c9c51b4

Observation d746886f-85c0-44c8-832b-26fd55b0bf98 · inbound

Beyond IID: How General Are Tabular Foundation Models, Really? cites this paper.

Beyond IID: How General Are Tabular Foundation Models, Really? MOMENT: A Family of Open Time-series Foundation Models

Reference 156

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arxiv_id, observed 2026-06-30T07:04:21.542993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T06:59:14.626274Z digest=sha256:4638e47775134ad4305f099ab4130aeb1c13edcefe2729910e9c13fe7e46c71a

Observation 0b72c38d-048f-410f-93c2-766712c27fde · inbound

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models cites this paper.

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models MOMENT: A Family of Open Time-series Foundation Models

Reference 41

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arxiv_id, observed 2026-07-01T09:05:37.153477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-01T06:48:25.850862Z digest=sha256:c67c67ae566a147053c4242544b526cc9ac10a18967fae0ca142c81c830aafd6

Observation d76ec23a-d820-4666-88fa-29e55b0b6872 · inbound

EVOTS: Evolutionary Transformer Search for Time Series Forecasting cites this paper.

EVOTS: Evolutionary Transformer Search for Time Series Forecasting MOMENT: A Family of Open Time-series Foundation Models

Reference 12

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arxiv_id, observed 2026-07-02T19:57:19.185496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-02T19:50:20.268930Z digest=sha256:5219dcfb1ba21ce2d456d9b21e9300a4bb948f634df86dd8fba44a9125e66c67

Observation 8662e74a-c8a4-4015-85dc-29304bd63839 · inbound

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning cites this paper.

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning MOMENT: A Family of Open Time-series Foundation Models

Reference 14

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arxiv_id, observed 2026-07-02T15:37:05.968695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-02T15:32:42.523362Z digest=sha256:b8192eecb1fa971fc5c73b1cc19b7cf0c17bacb9941729e881f00b68687bb686

Observation 3a487d64-1977-4292-a030-3d9040fb3b35 · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis MOMENT: A Family of Open Time-series Foundation Models

Reference 119

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arxiv_id, observed 2026-07-03T17:38:43.286236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-03T17:34:37.552706Z digest=sha256:ec585d3733e468f82b3657b566205e67ca664a5b009ac8adc1ea3359638237a6

Observation 4b8f9102-ea46-4eea-8ca6-abbaa1b772e1 · 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 MOMENT: A Family of Open Time-series Foundation Models

Reference 33

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arxiv_id, observed 2026-07-03T17:28:44.059236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-03T17:23:35.304926Z digest=sha256:a59309ad411a2970ade852de5d9675269d48c82f92ee82983c2f17327ccd63ce

Observation 781535af-56fc-491c-a310-cd2058bb3d00 · inbound

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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no resolver link, observed 2026-08-02T09:50:20.556896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:50:20.556896Z digest=sha256:b0718142691735ee71ccd87c8913345814fc3ec9b8d9dfa95f0213cd55edfc5a

Observation 97865c67-605c-4732-b4cf-01f0f3de72e2 · inbound

Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule cites this paper.

Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule MOMENT: A Family of Open Time-series Foundation Models

Reference 5

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no resolver link, observed 2026-08-02T09:17:11.928755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:17:11.928755Z digest=sha256:9224e957854f6e1135443a450680de93313e9fcefba4f6dd4f99ac2d1b0f2989

Observation 81240bed-3c51-4376-a1d5-2e96ffb466af · inbound

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers cites this paper.

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers MOMENT: A Family of Open Time-series Foundation Models

Reference 11

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no resolver link, observed 2026-08-01T04:51:44.096588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T04:51:44.096588Z digest=sha256:05b4aca2dc63117fb311d2ef07627d3b256311582417bfa7f88fbbdb41a80e15

Observation 69b3b5c3-ecd8-46b7-98e0-921d2c85c1e1 · inbound

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification cites this paper.

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification MOMENT: A Family of Open Time-series Foundation Models

Reference 14

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no resolver link, observed 2026-07-30T11:22:19.131499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T11:22:19.131499Z digest=sha256:23cb61bdb69d8e74c5564c6877dbfbec1de0341c5f492cd070743c73771a8888