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Paper Citation Record · LEDGER

DELPHYNE: A Pre-Trained Model for General and Financial Time Series

As of 16 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2506.06288.

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

pith.paper-citation-record.v1
2506.06288 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:15:53.522821Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

70 of 70 outbound references displayed

  • verified exact6
  • verified fuzzy32
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83296b75-7038-47a0-8d6b-4ffb37713d0d · outbound

This paper cites Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T \"u rkmen, and Yuyang Wang.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T \"u rkmen, and Yuyang Wang

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9577792b-542f-4532-a0f7-b968911185e3 · outbound

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

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Chronos: Learning the Language of Time Series

Reference 2

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Observation 488fa69d-cce6-459e-87cd-8c0d62f5c371 · outbound

This paper cites Machine Learning Methods for Inflation Forecasting in B razil: New Contenders versus Classical Models.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Machine Learning Methods for Inflation Forecasting in B razil: New Contenders versus Classical Models

Reference 3

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Observation acb2d441-8d67-4f25-b0e2-fa72ab65a473 · outbound

This paper cites Assimakopoulos and K.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Assimakopoulos and K

Reference 4

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verified exact
doi, observed 2026-08-15T22:15:53.654340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e34e58bc-912d-4856-baa3-7963a258b244 · outbound

This paper cites Generalized Autoregressive Conditional Heteroskedasticity , journal = Journal of Econometrics.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Generalized Autoregressive Conditional Heteroskedasticity , journal = Journal of Econometrics

Reference 5

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source=arxiv_source observed=2026-08-15T22:15:53.251670Z digest=sha256:b10a355e0e846470a36b8eab6f949d8f227c3abe8ae607cee44493bc8a4a0ac8

Observation 580bb813-fdc0-4d17-a00b-776a163587a3 · outbound

This paper cites an unresolved cited work.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-15T22:15:53.255847Z digest=sha256:c56a5ba283132664f50e06358dc506dd68d837fcb7ee5859efc4b19d30528edf

Observation bf959378-08dd-4002-a059-862c89b7d812 · outbound

This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 7

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source=arxiv_source observed=2026-08-15T22:15:53.261404Z digest=sha256:d43ca4afcbdbeb32dd544332578f235c810b631755f88d2d22fc4e02837d6f9f

Observation fc0fd589-9e6f-4c11-8de2-2e1980f45d8f · outbound

This paper cites C hat GPT Informed Graph Neural Network for Stock Movement Prediction.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series C hat GPT Informed Graph Neural Network for Stock Movement Prediction

Reference 8

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doi, observed 2026-08-15T22:15:53.625618Z

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Observation cca7dab0-10f7-4ae9-80bf-97d5c0b23d8b · outbound

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

DELPHYNE: A Pre-Trained Model for General and Financial Time Series A decoder-only foundation model for time-series forecasting

Reference 9

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Observation 85d379c7-bbd9-4ac3-a94e-449b71f06c7d · outbound

This paper cites CatBoost: gradient boosting with categorical features support.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series CatBoost: gradient boosting with categorical features support

Reference 10

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source=arxiv_source observed=2026-08-15T22:15:53.274105Z digest=sha256:84d89b8a062901d04fe483c4177910b969c9651b20e6fbc4e4a4bf8b85c595c1

Observation de1fbf4a-d06e-49b6-b1c0-a2c6c2353ec0 · outbound

This paper cites Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

Reference 11

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Observation 386594a4-b291-478d-b82a-4c483024cb52 · outbound

This paper cites Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

Reference 12

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Observation 5957a43a-c0a9-460b-af34-fda7ec261303 · outbound

This paper cites BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting

Reference 13

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1566ee78-9596-42c8-ad87-9557735f3667 · outbound

This paper cites Dish- TS : A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Dish- TS : A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting

Reference 14

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Observation 6da20ae3-08ea-4d5c-9efc-5c2919b6b37f · outbound

This paper cites TimeGPT-1.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series TimeGPT-1

Reference 15

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Observation ebc5f5e9-d829-49c3-a034-cdd22ab401b1 · outbound

This paper cites Strictly Proper Scoring Rules, Prediction, and Estimation.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Strictly Proper Scoring Rules, Prediction, and Estimation

Reference 16

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raw_fallback, observed 2026-08-15T22:15:54.678447Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation ed16f983-19c3-44b9-9608-ca19d855922c · outbound

This paper cites an unresolved cited work.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Unresolved cited work

Reference 17

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Observation 75c83a61-d75b-4fb3-a25d-294fdb1ccb9e · outbound

This paper cites Neuralfactors: A novel factor learning approach to generative modeling of equities.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Neuralfactors: A novel factor learning approach to generative modeling of equities

Reference 18

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Observation e744e430-474f-496a-acb8-f20201dff25f · outbound

This paper cites Unsupervised Model Selection for Time Series Anomaly Detection.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Unsupervised Model Selection for Time Series Anomaly Detection

Reference 19

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation fec982ac-583c-4609-a1e3-91dd8f15a70e · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series MOMENT: A Family of Open Time-series Foundation Models

Reference 20

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Observation dca4e6ef-70ef-4028-858c-91c99ae6667d · outbound

This paper cites Large Language Models Are Zero-Shot Time Series Forecasters.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Large Language Models Are Zero-Shot Time Series Forecasters

Reference 21

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raw_fallback, observed 2026-08-15T22:15:54.623250Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 05e23acd-b061-4481-b8f0-325d93202350 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Masked Autoencoders Are Scalable Vision Learners

Reference 22

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source=arxiv_source observed=2026-08-15T22:15:53.321576Z digest=sha256:9c9696b3393e33038d7d8dba03af673a8b46e691ff18078a807938780c1e454b

Observation a8a8a0c1-2e55-4943-b50f-bed239d604f7 · outbound

This paper cites Hoffman and Andrew Gelman.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Hoffman and Andrew Gelman

Reference 23

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation d42c969f-2710-463a-9d97-06e4e0c5aff2 · outbound

This paper cites Tab PFN : A Transformer That Solves Small Tabular Classification Problems in a Second.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Tab PFN : A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e8aa6476-6e02-4326-b993-1f7e15c20eda · outbound

This paper cites Deep Learning Volatility.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Deep Learning Volatility

Reference 25

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source=arxiv_source observed=2026-08-15T22:15:53.333044Z digest=sha256:8712245bc420574e017b1878cabc78eb2a805c60797c4cb2be7a69e20c3b8f8f

Observation ec2728c6-00bb-452d-b40d-dc5ebe42a9e5 · outbound

This paper cites Errors on Percentage Errors , 4 2014.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Errors on Percentage Errors , 4 2014

Reference 26

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raw_fallback, observed 2026-08-15T22:15:54.586829Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 5fed2d15-4107-45dc-96f2-c55594b75347 · outbound

This paper cites Another Look at Measures of Forecast Accuracy.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Another Look at Measures of Forecast Accuracy

Reference 27

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 71802070-e082-4ef0-a44e-270ab198800d · outbound

This paper cites Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen

Reference 28

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raw_fallback, observed 2026-08-15T22:15:54.559778Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b17a2fc6-c144-463a-ab98-37ee71a40c82 · outbound

This paper cites A Study of BFLOAT16 for Deep Learning Training.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series A Study of BFLOAT16 for Deep Learning Training

Reference 29

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source=arxiv_source observed=2026-08-15T22:15:53.348336Z digest=sha256:ba0f1e45cf1b25d832b9382a6658ce9164c939a33eaa1eea5951e792afe83b24

Observation 07936610-cc26-4a75-a3c9-130df427bc3e · outbound

This paper cites Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift

Reference 30

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raw_fallback, observed 2026-08-15T22:15:54.544981Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 691c31dc-2230-475d-bda4-90ef3c0344fc · outbound

This paper cites Aditya Prakash.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Aditya Prakash

Reference 31

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source=arxiv_source observed=2026-08-15T22:15:53.355914Z digest=sha256:5d4c02dd2b789fea37256a16bb0040ee753954044a9dbc0fdd5c53c41baac2ab

Observation 0aac2ff4-f568-4094-b7d4-eb802f763592 · outbound

This paper cites Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, and B.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, and B

Reference 32

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 22c284c5-314a-4e7e-a624-5a1eddbd1c6d · outbound

This paper cites LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Reference 33

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source=arxiv_source observed=2026-08-15T22:15:53.363469Z digest=sha256:05cdfb084a72b3226e45658cfcc38c8cd21b1e3e653100411598f782017cf6dd

Observation 7637065b-2cbd-4b1c-ae82-1a610d31e00c · outbound

This paper cites Large ST : A Benchmark Dataset for Large-Scale Traffic Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Large ST : A Benchmark Dataset for Large-Scale Traffic Forecasting

Reference 34

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raw_fallback, observed 2026-08-15T22:15:54.506532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.367768Z digest=sha256:22a9770571fbbb98b0f928a2fd885d531e11057def59cfce2348b8bf72248ff5

Observation 55d940b2-276b-4353-a075-5f6e21e8f5ae · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 35

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raw_fallback, observed 2026-08-15T22:15:54.494784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.373180Z digest=sha256:0ed6209679f0818e64a5cb7dfc0ff6058fe91b7adca3f4543bee9e5383fe6320

Observation bf4aba63-7773-465f-b6a8-58f6e0bebf2e · outbound

This paper cites NeuralBeta: Estimating Beta Using Deep Learning.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series NeuralBeta: Estimating Beta Using Deep Learning

Reference 36

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verified exact
local_arxiv, observed 2026-08-15T22:15:54.064794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.377138Z digest=sha256:3016f83b2a45101bbb8294908033b54efa7ce6ad4202da3c821e1d8f8f07d1f0

Observation bdee79a1-fbbb-49be-b71b-07e38aa5b1a1 · outbound

This paper cites Can chatgpt forecast stock price movements? return predictability and large language models.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Can chatgpt forecast stock price movements? return predictability and large language models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.381220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.381220Z digest=sha256:e7817307812522f360f85b21642a25015c619956b1d3a1a734a301ebb6189310

Observation 91786b51-37aa-4fcd-8528-0c1c5aeb476e · outbound

This paper cites The M4 Competition: 100,000 time series and 61 forecasting methods.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series The M4 Competition: 100,000 time series and 61 forecasting methods

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.385106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.385106Z digest=sha256:2618988f723be9a6eb2fde9036675983a874a5cc12b1f12e13e1b8c5b1f7ae15

Observation 238d2f8b-57cd-4df7-bd97-3db83236427f · outbound

This paper cites Position: Graph Foundation Models Are Already Here.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Position: Graph Foundation Models Are Already Here

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.475266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.389385Z digest=sha256:580d8d8863df2d0a088ae41500e435a7814113d2f2b47f022d9abfa5161baa6e

Observation 5f7c141c-e961-4f4f-901d-07d32e2d17ef · outbound

This paper cites Mouatadid, P.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Mouatadid, P

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.462662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.393755Z digest=sha256:9513cbd2cac5e5b68b50f993ccb333a50712db659e98e85a7d75aa5ee9df53b3

Observation 8298ef6a-404e-45bd-90cb-ddde3745f91d · outbound

This paper cites Transformers Can Do B ayesian I nference.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Transformers Can Do B ayesian I nference

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.450269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.397869Z digest=sha256:df57cae0e8e4c2f877dd08bcf18b911291b44cef0efcd5e8d99d1f8d69bf9e5e

Observation 1ef5a65f-269d-4c0e-a225-f6ffffc058e9 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.436304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.401738Z digest=sha256:281cd6c67280ee79de296e4167756fefeba5cab2507889b5ee033e20896f792c

Observation 6b034c1a-bc2a-4f38-ac04-e08dda55874d · outbound

This paper cites Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.425100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.406109Z digest=sha256:01f3ae89f4054e8bad1b40e7cd53e1e9a7b54bd14ec7407636bb9a0799ae60f5

Observation 85fe2a90-77ab-445c-96d5-085a86f990a9 · outbound

This paper cites Learning Quantile Functions without Quantile Crossing for Distribution-Free Time Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Learning Quantile Functions without Quantile Crossing for Distribution-Free Time Series Forecasting

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.413653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.410090Z digest=sha256:a6535357310672480dca0d6df1d5850554e008f350bdd865dd7421c8e28ff463

Observation ca4f9594-1852-47c7-af69-e7139781e52c · outbound

This paper cites Deep Learning for Volatility Forecasting in Asset Management.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Deep Learning for Volatility Forecasting in Asset Management

Reference 45

Resolution
verified exact
doi, observed 2026-08-15T22:15:53.597251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.415014Z digest=sha256:166324df31e32b2cdeea383f3fa0bc20112f4b5753925343619e5e8ec37e945a

Observation 50832eea-3297-4375-b887-8cad373c4b12 · outbound

This paper cites Lag- L lama: Towards Foundation Models for Time Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Lag- L lama: Towards Foundation Models for Time Series Forecasting

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.402208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.419072Z digest=sha256:321abba37c7714d38a6dd70de7da834fa590738b3c57ec39b333dd328a6682aa

Observation b5be95f8-347c-46ff-bda5-75de8f4f9660 · outbound

This paper cites DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.426513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.426513Z digest=sha256:e5f4d8935992d2218a3bce9d6f10cd148a787d4b50060dd018a44b5ac79b426c

Observation 0bfcc058-c1cf-4f93-92cf-89b13b378ecc · outbound

This paper cites GLU Variants Improve Transformer.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series GLU Variants Improve Transformer

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.430359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.430359Z digest=sha256:a941d5cfb684772a73756946b9061cd2ca44e28c8e8a1268f8b3d763979480ed

Observation 088c39fc-30c7-4235-a5aa-220744d5eb14 · outbound

This paper cites RoFormer: Enhanced transformer with Rotary Position Embedding.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series RoFormer: Enhanced transformer with Rotary Position Embedding

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.434253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.434253Z digest=sha256:e3a20b367973c2d8e663744890bcf03f67d1e240c42bae64b4a874eef630b37e

Observation 12e8a5fa-d51d-473b-b4cc-e15a3be842bc · outbound

This paper cites Generative Machine Learning for Multivariate Equity Returns.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Generative Machine Learning for Multivariate Equity Returns

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.439108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.439108Z digest=sha256:2410c16a05628f28f6f4d8c2545d7629a80e75e60d68043409bace42d9505c50

Observation dd6be8a5-11a8-4a88-a361-7f33cd272152 · outbound

This paper cites ElectricityLoadDiagrams20112014.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series ElectricityLoadDiagrams20112014

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.390533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.442705Z digest=sha256:9ea0c348f3681b4276785ba9771c9b6969e81c6c8615d9f4de8997e9fd05a627

Observation b08da31e-7a0d-471b-8adc-6ee7a1e5e099 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series WaveNet: A Generative Model for Raw Audio

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.448250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.448250Z digest=sha256:88003faaddb524cae26ee29c79129e8d46d086fdabbae049d08e334b970a5614

Observation 7b90e135-2525-41e6-b60f-8d382421f390 · outbound

This paper cites Cross-Frequency Time Series Meta-Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Cross-Frequency Time Series Meta-Forecasting

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-15T22:15:53.840331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.452702Z digest=sha256:572d71836fbc499a2c95c698986e558b7f59899dfbf12fba29f9809cbfa9700e

Observation 762734cd-de35-4b5e-9480-2f337f2e448d · outbound

This paper cites LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.457722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.457722Z digest=sha256:4738f2103455c303cd1d7bb71c822c7e10b39d8d9113cfa383d3060f93f3d3e6

Observation 0d71607f-4498-43b2-8bce-b6b6a8b034a3 · outbound

This paper cites Subgraph Pooling: Tackling Negative Transfer on Graphs.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Subgraph Pooling: Tackling Negative Transfer on Graphs

Reference 56

Resolution
verified exact
doi, observed 2026-08-15T22:15:53.576513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.461870Z digest=sha256:a732c48cfb86799da2d371a2d8f0d91e508a718c086d6f6edd4822d86bf4624a

Observation ecf4d25f-1b2b-40e7-9639-32869a1834e6 · outbound

This paper cites Benchmarks and Custom Package for Electrical Load Forecasting , 2024 b.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Benchmarks and Custom Package for Electrical Load Forecasting , 2024 b

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.377687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.465774Z digest=sha256:c93723a5d221a57a658071db89554d394cd8b89e158dc5095f3a39e68dda516f

Observation 8ccf5f6a-8b80-49c8-a7e3-494ae298a6a0 · outbound

This paper cites Characterizing and Avoiding Negative Transfer.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Characterizing and Avoiding Negative Transfer

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.469939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.469939Z digest=sha256:9299202d167201249e8689de5f9910b4d80c29a31c60fb6c5913c20a1e1b6f69

Observation e1cda11c-76d4-46a2-98dc-60d68f14b12f · outbound

This paper cites Transformers in Time Series: A Survey.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Transformers in Time Series: A Survey

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.473799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.473799Z digest=sha256:57370914017d18005ce9b663882c62ef1dfa197b661827f0b0fdb767b7141957

Observation 260b3ae7-7fe2-49d4-a8a5-2dd60930ea18 · outbound

This paper cites Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.477817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.477817Z digest=sha256:47a04c6315398ef40940d9eb18f0b66a91f72476ada1c474af79d21eeb2ab780

Observation 3506877e-2147-4f98-8d53-0cff9fcbf6ac · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Unified Training of Universal Time Series Forecasting Transformers

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.364903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.481893Z digest=sha256:9996960f07125e3bf4bff93e51eab57090b9a71e4b79af94c89240f9fda7082c

Observation b9ef38ab-648a-4a00-8264-c4dbd66a0fd2 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.351744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.485574Z digest=sha256:f0b8a7db2590ab17af7306e936ff79d11c1b4b934dbeacd904c6e3977b1439ff

Observation 21499924-52f3-4e07-95a4-e13db166584a · outbound

This paper cites On Layer Normalization in the Transformer Architecture.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series On Layer Normalization in the Transformer Architecture

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.339569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.489172Z digest=sha256:ce70b08170a4955c8569598f130f734c8d0cfa035fc8d95bf7d6bf9c48b51913

Observation 5649260c-fd54-4bdf-ba9a-a88b5c47199d · outbound

This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Temporal regularized matrix factorization for high-dimensional time series prediction

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.327352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.493368Z digest=sha256:d081b7b1529cfffc16fffc7cde6b1f83139687964e98cea8adbf05c1f2312d74

Observation 0c877836-0d29-4c60-8506-41c525926e5d · outbound

This paper cites Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.497148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.497148Z digest=sha256:af7f42483e82e2c3b41566875bef2deafb6376d67a9838882031b5df540b87b6

Observation dd6ba417-64c8-4034-aeaf-487b196cec17 · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.501769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.501769Z digest=sha256:794e939b963e08a10b978067ae9ed67c8311a3e808a72e5d1da9c042dd8e6080

Observation 5ce2a849-e0a9-4baa-bde3-258989eb64ff · outbound

This paper cites FED former: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series FED former: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.313957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.505923Z digest=sha256:fb91279f8538b7d783840e5b63dd5638e24c8437190913600a3c3f86c3222cd1

Observation 07a739cc-1d56-4a78-b34e-b147e35393bd · outbound

This paper cites One Fits All: Power General Time Series Analysis by Pretrained LM.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series One Fits All: Power General Time Series Analysis by Pretrained LM

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:15:54.302293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:15:53.509829Z digest=sha256:40994e1976259527fd5d9b7e5485e7eaa99272947872675a02d68e26e321b2d2

Observation 2030cf20-c94e-42cb-a0b6-9d2bc63a9927 · outbound

This paper cites @esa (Ref.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series @esa (Ref

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.513553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.513553Z digest=sha256:a1d180a5f68f5028a7f13ef742fd74c9cb971b5a1e50cf8de9106d22dc6575f9

Observation 322492c2-88ef-4435-be53-d8834c933c66 · outbound

This paper cites an unresolved cited work.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Unresolved cited work

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.518490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.518490Z digest=sha256:fe7ce98af01a67273441de02e5dcddd524a3352a5a26a3700daba7e64d087696

Observation ff04a22a-a18e-4d2a-8524-9389cfd972c2 · outbound

This paper cites Aggregated.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series Aggregated

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-15T22:15:53.522821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:15:53.522821Z digest=sha256:e9c554783b1800d6577fe2c591e7ebdc1d79cc6a6ec9511d80135bb2ccb9310e

Pith citing papers

No inbound Pith citation observations are available.