Unified Training of Universal Time Series Forecasting Transformers | Paper Citation Record

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

Chronos: Learning the Language of Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-13T08:27:23.448966Z

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Observation 03b6047d-e47a-4663-963a-0d0d9cc688ba · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark Unified Training of Universal Time Series Forecasting Transformers

Reference 201

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:05:51.364339Z

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

Sundial: A Family of Highly Capable Time Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:33.823042Z

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LAST SToP For Modeling Asynchronous Time Series cites this paper.

LAST SToP For Modeling Asynchronous Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 55

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Transformers and Their Roles as Time Series Foundation Models cites this paper.

Transformers and Their Roles as Time Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 1

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HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting cites this paper.

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 52

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

Byte Pair Encoding for Efficient Time Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 10

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When can isotropy help adapt LLMs' next word prediction to numerical domains? cites this paper.

When can isotropy help adapt LLMs' next word prediction to numerical domains? Unified Training of Universal Time Series Forecasting Transformers

Reference 9

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Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models cites this paper.

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 15

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Observation f6fc4136-6d21-4862-9d89-9c7dc4a522c2 · inbound

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

LightGTS: A Lightweight General Time Series Forecasting Model Unified Training of Universal Time Series Forecasting Transformers

Reference 2012

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

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 1

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Observation 5ee91e22-2d09-4819-a54d-1c35bd1af9d7 · inbound

Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting cites this paper.

Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 18

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Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions cites this paper.

Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions Unified Training of Universal Time Series Forecasting Transformers

Reference 16

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Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:21.638310Z

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Towards Interpretable Time Series Foundation Models cites this paper.

Towards Interpretable Time Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:41.887615Z

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Observation f0258da3-8cbe-48fc-9157-750cb571ecfd · inbound

FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction cites this paper.

FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction Unified Training of Universal Time Series Forecasting Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:58.901384Z

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STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements cites this paper.

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements Unified Training of Universal Time Series Forecasting Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T00:52:10.719012Z

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Observation ce396509-4cca-4a1c-8793-2c01ab250045 · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 55

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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 Unified Training of Universal Time Series Forecasting Transformers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T23:57:19.751078Z

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Observation d12ddd37-c5d7-4165-8bc5-ac83f732ec78 · inbound

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis cites this paper.

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis Unified Training of Universal Time Series Forecasting Transformers

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:24:21.612779Z

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Observation 47bf058b-a3bb-47b1-bd10-f01864db9409 · inbound

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis cites this paper.

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis Unified Training of Universal Time Series Forecasting Transformers

Reference 41

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Observation 87e42c42-6af3-4148-a0ee-ed6e63bd41ab · inbound

TSVer: A Benchmark for Fact Verification Against Time-Series Evidence cites this paper.

TSVer: A Benchmark for Fact Verification Against Time-Series Evidence Unified Training of Universal Time Series Forecasting Transformers

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:00:34.038631Z

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Observation 06f43c22-9aaf-441a-bd71-549c4e3992b0 · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:50:10.931229Z

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Observation 5c98fba6-501c-44e3-9b19-90ce1b791dc4 · inbound

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

TempusBench: An Evaluation Framework for Time-Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:05.556131Z

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Observation 4e08fce7-7ce4-42e5-8377-5d16b70213b6 · inbound

Thermal-GEMs: Generalized Models for Building Thermal Dynamics cites this paper.

Thermal-GEMs: Generalized Models for Building Thermal Dynamics Unified Training of Universal Time Series Forecasting Transformers

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:15:48.522914Z

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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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Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models cites this paper.

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-09T04:40:12.849517Z

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RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction cites this paper.

RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction Unified Training of Universal Time Series Forecasting Transformers

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:28.387962Z

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

Observation f88d4e92-7d52-47ae-b550-6fa69be6f947 · inbound

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting cites this paper.

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:56:15.137818Z

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

Observation 1a1a2c2f-1e9f-4592-a47f-40187314e899 · inbound

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

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning Unified Training of Universal Time Series Forecasting Transformers

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.574799Z

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

Observation 67198258-d694-4fac-80ea-d2e52ab9e176 · inbound

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series cites this paper.

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:52:25.844164Z

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Observation 736887d8-6a3d-4833-bea9-d9a1288de54a · inbound

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series cites this paper.

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:18:00.393977Z

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Observation 2e44175f-0d5d-4d4a-aadb-c417d76c8b80 · inbound

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series cites this paper.

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:45.848520Z

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Observation 17f5f78d-b3a6-417d-a824-8d25e4666fc8 · inbound

Forecasting megaelectron-volt electron flux in the Earth's outer radiation belt using supervised machine learning algorithms and a timeseries foundation model cites this paper.

Forecasting megaelectron-volt electron flux in the Earth's outer radiation belt using supervised machine learning algorithms and a timeseries foundation model Unified Training of Universal Time Series Forecasting Transformers

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-19T19:42:44.166410Z

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Observation fdad080a-e451-45ed-a061-5b78ed59f367 · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:54:45.205941Z

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Observation 5e41cf0f-80af-4c83-8ab7-6cc609e01284 · inbound

LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support cites this paper.

LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support Unified Training of Universal Time Series Forecasting Transformers

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.257536Z

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Observation 2c60f887-10d7-4f7b-8f9a-38986bbf0e05 · inbound

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP cites this paper.

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP Unified Training of Universal Time Series Forecasting Transformers

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-07-01T16:05:48.940103Z

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

Observation f2b23e05-7226-412d-9042-22033622c674 · inbound

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

Does Normalization Choice Matter for Causal Large Time-Series Models? Unified Training of Universal Time Series Forecasting Transformers

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:07:28.562928Z

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Observation 9c9f0c31-f188-4839-8668-e51a3b504306 · inbound

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms cites this paper.

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms Unified Training of Universal Time Series Forecasting Transformers

Reference 18

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Observation 79e32ba3-ca1c-4e10-9b12-4ca9ae2d5857 · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:27:36.224501Z

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Building Social World Models with Large Language Models cites this paper.

Building Social World Models with Large Language Models Unified Training of Universal Time Series Forecasting Transformers

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T09:07:47.511079Z

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Observation 9cd4f6b3-b4e5-4d3e-9613-cff91dba41b6 · inbound

From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol cites this paper.

From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol Unified Training of Universal Time Series Forecasting Transformers

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:39:58.525973Z

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

Observation a433b5f0-9a98-4d16-a4a9-57cef6cb949f · inbound

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

EVOTS: Evolutionary Transformer Search for Time Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:57:19.191338Z

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

Observation a268ccf7-4184-4b9b-be6e-7aac2940a030 · inbound

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

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Unified Training of Universal Time Series Forecasting Transformers

Reference 103

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:43.390890Z

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

Observation 49671364-5f51-40e4-b437-947875915ddf · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.118753Z

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Observation 511a9f19-18fb-4f75-bbbf-630ed01aeaf9 · inbound

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting cites this paper.

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting Unified Training of Universal Time Series Forecasting Transformers

Reference 3

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Observation 8c158a42-4004-47a3-8fd3-9a3fe727d234 · inbound

Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.

Modular Foundation Models for Time-Series Perception in Digital Twins Unified Training of Universal Time Series Forecasting Transformers

Reference 14

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Observation cbd02bb7-b0c6-4508-b331-93e6fc4b6944 · inbound

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods cites this paper.

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods Unified Training of Universal Time Series Forecasting Transformers

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T22:35:52.236397Z

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Observation 00df699c-e3e6-4138-9a98-9402b6ca383a · 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 Unified Training of Universal Time Series Forecasting Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T09:17:11.919277Z

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Observation d1fc1905-ca5d-401a-b76e-4a18b6df06ab · inbound

Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail cites this paper.

Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail Unified Training of Universal Time Series Forecasting Transformers

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T01:54:53.857022Z

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Observation fb0428cb-4cf2-41c0-be6a-fe4601985606 · inbound

Align-RAG: Alignment Is All You Need for TSFM In-Context Learning cites this paper.

Align-RAG: Alignment Is All You Need for TSFM In-Context Learning Unified Training of Universal Time Series Forecasting Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T10:21:59.356270Z

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