Paper Citation Record · LEDGER

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 45 inbound Pith citation observations for arXiv:2409.16040.

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

pith.paper-citation-record.v1
2409.16040 v4

Coverage vector

measured 0 of 0 reference resolution

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measured 45 of 45 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 45 of 45 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:06:03.258620Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

10
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3867702d-9f07-412b-858e-643e0152e4c3 · inbound

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

Investigating Compositional Reasoning in Time Series Foundation Models Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 44

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Observation 744f831a-1457-4ead-96fa-eecee25f9380 · inbound

Harnessing Vision Models for Time Series Analysis: A Survey cites this paper.

Harnessing Vision Models for Time Series Analysis: A Survey Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 46

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Observation a2114799-6c2c-4206-8355-fe6fb5e2155e · inbound

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

BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 39

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Observation f162fd9e-bbe7-4558-8095-a96b64548eaf · inbound

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities cites this paper.

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 34

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EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media cites this paper.

EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 30

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Observation fb11e14e-40a8-41b8-b009-9be979d82fcb · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 22

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Observation 8e802fe5-4bee-4ec8-8294-0adb838c78f0 · inbound

dots.llm1 Technical Report cites this paper.

dots.llm1 Technical Report Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 37

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Observation ca5bedd7-38d3-45a4-ab2d-cce4b69cce62 · inbound

LightGTS: A Lightweight General Time Series Forecasting Model cites this paper.

LightGTS: A Lightweight General Time Series Forecasting Model Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 14

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Observation 42e46a8a-58cb-44a3-865c-d345a4862f62 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 114

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Observation 7d502245-2e05-4bd6-b8e3-cc9534cc1e38 · inbound

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting cites this paper.

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 6

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Observation 81eb8668-f7d1-431a-8680-734725fb47cf · inbound

From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 90

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Observation b5b7d91b-c543-47dd-9466-0ce8351c0bfd · 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? Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 30

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Observation 61dbc738-e621-4ac1-8929-fe1b4f02b81b · inbound

Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge cites this paper.

Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 11

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Observation b9c5e804-d71a-46b5-8aac-d22feeecb0a2 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 23

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Observation 6d2ae19f-4a01-4aae-b175-88bda3b70e72 · inbound

Foundation Models for Demand Forecasting via Dual-Strategy Ensembling cites this paper.

Foundation Models for Demand Forecasting via Dual-Strategy Ensembling Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 46

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Observation 07d32506-eb27-4062-9a97-8358ffaa17ca · inbound

Foundation Models for Clean Energy Forecasting: A Comprehensive Review cites this paper.

Foundation Models for Clean Energy Forecasting: A Comprehensive Review Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 66

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Observation 9b3b6e27-db4b-463b-9821-aa2358b7694b · inbound

Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain cites this paper.

Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T23:47:25.833757Z

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Observation 2978db32-6b1f-4303-a8fa-896eba7e4a14 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 26

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Observation 634c905d-8726-4272-bafe-0a09b86aa2c6 · inbound

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction cites this paper.

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 38

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Observation adc31fd8-d8ad-4b0f-8603-c30e4c60c06e · inbound

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction cites this paper.

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:36:41.920552Z

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Observation 8b291c03-3bb9-412d-a8d0-bca30572488f · inbound

FinCast: A Foundation Model for Financial Time-Series Forecasting cites this paper.

FinCast: A Foundation Model for Financial Time-Series Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 31

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Observation 875d2aba-456f-433a-a8df-83e318b7f895 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 11

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Observation b81776a7-30ad-4093-8906-6a196b6c5f7e · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:25:34.106327Z

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Observation 505a199a-a8ff-4533-a4e7-c1fb657eda2d · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 36

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Observation aebaafcf-d66f-481b-b1bc-7faa0127dded · inbound

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models cites this paper.

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 10

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Observation e626993e-bb61-4259-9e1c-153cfd0cb8df · inbound

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning cites this paper.

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 2024

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Observation aac59592-cbce-4722-ba4c-516f09b76a28 · inbound

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

TS-Arena -- A Live Forecast Pre-Registration Platform Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:01:13.262368Z

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

Observation 23638b42-b894-4d14-a826-bedacc04b687 · inbound

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting cites this paper.

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 15

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Observation c171e890-0602-440f-876e-d987b8aab6a2 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 47

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Observation b8d8087e-a51f-40b4-8344-322932dc1863 · inbound

Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring cites this paper.

Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 72

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Observation bbb79feb-7032-41ff-8c62-9f1c3a99b1e3 · inbound

Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation cites this paper.

Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:50:47.009707Z

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Observation 8d45d876-3af6-4af6-ade9-295afdf4a17d · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:51:28.867724Z

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Observation ec1e423b-a006-4c8b-a7a9-557d9212def8 · inbound

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling cites this paper.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 48

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Observation 20a577fa-85ac-4618-b68b-86e08a7aa39b · inbound

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

Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:43:22.395754Z

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

Observation 564d5287-7ba1-42f7-85bd-819de4b8c4ab · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:16:12.885997Z

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Observation 0a0b2881-f31c-4048-a0b7-e9f745ba2c42 · inbound

Assessing the Operational Viability of Foundation Models for Time Series Forecasting cites this paper.

Assessing the Operational Viability of Foundation Models for Time Series Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:54:45.217790Z

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

Observation 579ada5f-e852-4e6d-8764-bcc138b07265 · inbound

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting cites this paper.

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-28T17:22:24.553655Z

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

Observation 208a1335-2346-4d11-af29-5cad663db63a · inbound

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting cites this paper.

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:27:26.149706Z

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

Observation e3cb6535-2dcb-4ec7-bda6-143015b847e7 · inbound

Does Normalization Choice Matter for Causal Large Time-Series Models? cites this paper.

Does Normalization Choice Matter for Causal Large Time-Series Models? Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:07:28.561835Z

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

Observation 8b3b3942-1e9c-44dc-94b1-b249836148c8 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:27:36.233567Z

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

Observation 0aa03cde-0d33-410a-b7ac-3404db60c8b5 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.518986Z

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 4075a79a-87e3-4806-84fd-67140beec052 · inbound

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition cites this paper.

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:08.998406Z

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 c37b5b76-0388-4043-9d87-7cf6583fcfe7 · 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 Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.078100Z

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

Observation bf2dbf65-8e63-4a51-b02b-6821e28bef31 · inbound

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

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 28

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Observation 8bc921fe-89b9-43e0-a358-d81d0405d82a · inbound

Crossing-Free Probabilistic K-Line Forecasts Without Retraining cites this paper.

Crossing-Free Probabilistic K-Line Forecasts Without Retraining Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 14

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Unavailable: canonical work link unavailable.