Pith. sign in

Paper Citation Record · LEDGER

Time Series Foundation Models for Multivariate Financial Time Series Forecasting

As of 7 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 2 inbound Pith citation observations for arXiv:2507.07296.

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

pith.paper-citation-record.v1
2507.07296 v1

Coverage vector

measured 100 of 117 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:49:30.906268Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:43:05.341575Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T19:34:06.502041Z

Reference resolution

100 of 117 outbound references displayed

  • verified exact20
  • verified fuzzy0
  • unresolved74
  • parse uncertain0
  • malformed identifier6
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8061b41b-9dd5-4323-839f-782ed8951645 · outbound

This paper cites A combination of artificial neural network and random walk models for financial time series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A combination of artificial neural network and random walk models for financial time series forecasting,

Reference 1

Resolution
verified exact
doi, observed 2026-08-06T18:49:36.663867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:15.195868Z digest=sha256:5d6774595f3efdcd3685ff72bdeb5e1d464277742c0ea18e1d4a480e52622dfc

Observation d800fb2c-ba5d-42b8-8c75-8341456b1622 · outbound

This paper cites Financial time series forecasting: A comprehensive review of signal processing and optimization-driven intelligent models,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Financial time series forecasting: A comprehensive review of signal processing and optimization-driven intelligent models,

Reference 2

Resolution
verified exact
doi, observed 2026-08-06T18:49:36.468382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:15.249450Z digest=sha256:8b4bbfcff73a1bec4e79b2abf0f7db7449e204f66a76fd86b86530ae1ecde919

Observation 0c577504-767d-4916-9170-e77bbf9db945 · outbound

This paper cites Makridakis, “Time series prediction: Forecasting the future and understanding the past andreas s.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Makridakis, “Time series prediction: Forecasting the future and understanding the past andreas s

Reference 3

Resolution
verified exact
doi, observed 2026-08-06T18:49:36.278555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:15.375670Z digest=sha256:840a5b2056d2241a20fb0c44df3e7795553646c6b12a830b91424e5ce873f1f8

Observation bec6637e-4979-4d1b-9069-e6e6522c5eaa · outbound

This paper cites Forecasting economic time series using targeted predictors,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Forecasting economic time series using targeted predictors,

Reference 4

Resolution
verified exact
doi, observed 2026-08-06T18:49:36.056641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:15.504922Z digest=sha256:02eb1d3d2b27e1b1e446abcce54044b87f13fcdb9d01d4d4d72feba9bfae805c

Observation a4029216-4aa4-4246-b7d8-275a003db10c · outbound

This paper cites Weather forecasting with ensemble methods,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Weather forecasting with ensemble methods,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:15.661164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:15.661164Z digest=sha256:10e515278d30612c015928c6505531a0a13bd4e7676de1fbeedbf99d8b17ff2d

Observation 3030b79a-24b9-4f04-bd86-01e99cb9b0ca · outbound

This paper cites Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:15.762022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:15.762022Z digest=sha256:93402dac9fe45f798f6a112ad46a278e9ac23ac8a382575890bba68af30e4c8b

Observation ad02e54c-c8c4-4d0a-9fa5-6d34eda44f62 · outbound

This paper cites Forecasting the future: A comprehensive review of time series prediction techniques,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Forecasting the future: A comprehensive review of time series prediction techniques,

Reference 7

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T18:49:35.903843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:15.954845Z digest=sha256:61ae8a605e40cd1e46f33b545044a95c8d94acee83a3ac4ac5462071f2f4910c

Observation 2e9e9ac5-76b4-458b-a5d4-6ac3970f54a6 · outbound

This paper cites Deep learning-based time series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Deep learning-based time series forecasting,

Reference 8

Resolution
malformed identifier
no resolver link, observed 2026-08-06T18:49:16.056385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.056385Z digest=sha256:2bd8dbd681caf09ddbac63f4c864d28764c9ada704fe4833ad56935b32ad38c8

Observation 60d345ea-5e39-4583-b199-a6fe6b562c34 · outbound

This paper cites Traffic flow prediction with big data: A deep learning approach,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Traffic flow prediction with big data: A deep learning approach,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:16.186037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.186037Z digest=sha256:778d241dbb43008fd7495fa42abc5e17c7ee5962d67f1f66c18105a5688143bf

Observation 087d1349-fc1f-42cd-9a4f-60a65886d3b7 · outbound

This paper cites A deep learning based stock trading model with 2-d cnn trend detection,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A deep learning based stock trading model with 2-d cnn trend detection,

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:16.340642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.340642Z digest=sha256:16b17ff7f94b5b6a17f793c663fcdd03e0fb505f1988ce27144fc0e815c67d6d

Observation 2351f62a-2040-4c73-b403-f293c0471992 · outbound

This paper cites Convolutional neural networks for forex time series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Convolutional neural networks for forex time series forecasting,

Reference 11

Resolution
verified exact
doi, observed 2026-08-06T18:49:35.749210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:16.524504Z digest=sha256:423fd3b5bffff49bf8eb4ff67febcd2347992d32b7c579f58446fe699923dfcb

Observation e2001b7b-40d0-4132-a525-344cc8aeb65d · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:16.656831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.656831Z digest=sha256:52a3da423e58331e2fbb0671ae736d5023d181e556d357eacf811141f1e71b4c

Observation 9712a5c6-fcea-4dfa-a706-ce8c9f0246cf · outbound

This paper cites Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:16.793722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.793722Z digest=sha256:5a16d974002817761d09679095137e0c4073edd7c774be24617b8b4602ae30c6

Observation 95ab4073-48fc-41df-9198-450d14b1d759 · outbound

This paper cites Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:16.921394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:16.921394Z digest=sha256:14b00be0f86db84472b2e6fc9e49ad2e0326b26206265ee5375601a62fcaa293

Observation 744a001c-e896-4db4-9d38-df590ce67dcb · outbound

This paper cites Learning long-term dependencies with gradient descent is difficult,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Learning long-term dependencies with gradient descent is difficult,

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:17.100262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:17.100262Z digest=sha256:8b90ef4d3bc089d724d7a07894dbed1fc3f52a9b69282b8be8a95bbf9c7f1553

Observation 07f9035c-eb64-40ef-95d5-6054bea35f38 · outbound

This paper cites On the difficulty of training recurrent neural networks,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting On the difficulty of training recurrent neural networks,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:17.237749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:17.237749Z digest=sha256:8ac397ddd76ebd16d9b78127457a5d3c4edb848c1c0f1666294c05c3e1496f38

Observation c27daeb2-b5f9-49ce-8283-dd53511adf9d · outbound

This paper cites Building trend fuzzy granulation-based lstm recurrent neural network for long-term time-series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Building trend fuzzy granulation-based lstm recurrent neural network for long-term time-series forecasting,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:17.395222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:17.395222Z digest=sha256:3adc117a00e0000b105d5695fea894cc1079a13a199b770f7b3969249f1cdcd6

Observation 885d85ff-d1ae-4dc2-908b-84f3b4c0ca00 · outbound

This paper cites Assessment of deep recurrent neural network-based strategies for short-term building energy predictions,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Assessment of deep recurrent neural network-based strategies for short-term building energy predictions,

Reference 18

Resolution
verified exact
doi, observed 2026-08-06T18:49:35.549554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:17.675178Z digest=sha256:5216f44804871ce23d7efa9f97235aa46eef34257eec57acb15a69244ec220bb

Observation 4f5bade8-0cf9-4418-8ae8-2776dd3adb65 · outbound

This paper cites Attention Is All You Need.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Attention Is All You Need

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:17.773141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:17.773141Z digest=sha256:7ec8b428ed6c8035ef9859dfb5a299eaf99e96c9cd88ffa04132cf5c0c1fa427

Observation 21bad38e-47f8-4542-9fb3-c60167e487b5 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:17.891552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:17.891552Z digest=sha256:ef1bbea47769389d786b6726931089fedc2d1ef864c087f12cb801bc6c6f6bea

Observation e9dba748-ccec-4a99-ab6e-32f943acae96 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.036369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.036369Z digest=sha256:26402dd04c06e1f5df1cb7031bbeb57395a1bfa802157a4b38431ca40cfeb683

Observation 87584baa-a826-4c29-b126-e968deaa42b9 · outbound

This paper cites Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.158760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.158760Z digest=sha256:b19bf6f4d4823a3fb33eb49c359f7cba2f1b310deb10012d03e1c0d39bc7f9d1

Observation 9ab5f6c3-2934-4f25-bd08-be64240c9a20 · outbound

This paper cites Adversarial sparse transformer for time series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Adversarial sparse transformer for time series forecasting,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.280669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.280669Z digest=sha256:454652e9ad8e369fe602662bed10760c98e095a65f9dbdb225c4005485b95aeb

Observation ae9fbbd1-5f47-45d6-9e4e-8835561d16d6 · outbound

This paper cites Anomaly transformer: Time series anomaly detection with association discrepancy,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Anomaly transformer: Time series anomaly detection with association discrepancy,

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.378780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.378780Z digest=sha256:d632e40181f6471411773e1ade71ad87a7b26b1c628a200cfb90941e3f7997e9

Observation 32b8fce9-fdee-44bc-8b2f-9e99cac82484 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.450769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.450769Z digest=sha256:8304203cb12c45585830c1c454d5189d14ad682c12022de61de4651a4bfaa612

Observation 83f98d1a-173b-4d1d-b913-a873ef177a78 · outbound

This paper cites Are Transformers Effective for Time Series Forecasting?.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.579708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.579708Z digest=sha256:862b8da060b870357d1769e471c1b17ae247dc643cf4628b4c804e9042bbabc5

Observation 466bd148-82df-44f4-a89e-536ee6a61819 · outbound

This paper cites A systematic review for transformer-based long-term series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A systematic review for transformer-based long-term series forecasting,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:18.706782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:18.706782Z digest=sha256:d508ed06869c74aaceebc2e3094849dd54efc2d72a25da439c1d3025b61896e2

Observation 480677f3-6872-4cce-8d3e-e9381ea89b8c · outbound

This paper cites Interpretation of Time-Series Deep Models: A Survey.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Interpretation of Time-Series Deep Models: A Survey

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:19.054467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:19.054467Z digest=sha256:26ebd89d5c1320a14dd0986295703acf3cfae1dcf1c313e8db4edd49e54539bc

Observation f026f601-1a00-471e-b2d2-7bbcd883c149 · outbound

This paper cites an unresolved cited work.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unresolved cited work

Reference 29

Resolution
verified exact
doi, observed 2026-08-06T18:49:35.283410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:19.256836Z digest=sha256:a9e76536a11e71c192e914659a44a2e101db8b14871cbe5fd5009a6ba41c27a6

Observation b1f829b6-8f7b-4d19-886d-d59f00429bdf · outbound

This paper cites Does the performance of banking sector promote economic growth? a time series analysis,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Does the performance of banking sector promote economic growth? a time series analysis,

Reference 30

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T18:49:35.065502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:19.384986Z digest=sha256:a86d978ea399513db5fb2c78886ed2f2f4818bd4e54cd78b5e8bfcdbadb96c6b

Observation 01e39ae7-8eb5-475e-8f57-25be9975c60a · outbound

This paper cites Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:19.530883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:19.530883Z digest=sha256:f1dfc27be3836701de6f280082bec2a9731a0a50bcd3e9dbcfafab37ca57f8e9

Observation 275bd993-94b5-45fd-b055-4911b950a57c · outbound

This paper cites Baltruˇsaitis, C.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Baltruˇsaitis, C

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:19.670765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:19.670765Z digest=sha256:c1e5cb191ff0b16653690b4cf5da90843980f058ddc29bd9a653515c45a6878a

Observation 14f5e530-f36a-4161-8b40-6f2f4f5769ab · outbound

This paper cites Deep unsupervised domain adaptation with time series sensor data: A survey,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Deep unsupervised domain adaptation with time series sensor data: A survey,

Reference 33

Resolution
verified exact
doi, observed 2026-08-06T18:49:34.869139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:20.063593Z digest=sha256:132f21bcd2210d2e6597591ea723695e4756dc24d5b785fcbcfa3c4cfe1f1342

Observation d48d7440-29be-4d18-9598-2a2dd9403ded · outbound

This paper cites Language models are unsuper- vised multitask learners,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Language models are unsuper- vised multitask learners,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:20.192521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:20.192521Z digest=sha256:4149cef870b77290eee556af8f3d502505a364d32b1a72c8304ebf66f79a681e

Observation 039bb375-ec02-424c-a5fe-0d01b8c69183 · outbound

This paper cites All in One: Multi-task Prompting for Graph Neural Networks.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting All in One: Multi-task Prompting for Graph Neural Networks

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:49:34.716751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:20.350908Z digest=sha256:ec644eaf1e5de44b61e12dbbc77227b24deb23f765295a24e1a4942cfc3f4399

Observation 10be6022-80ad-478e-9b22-c7b45666c291 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting LoRA: Low-Rank Adaptation of Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:20.553973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:20.553973Z digest=sha256:e59a49eb3470d90c0aaf262e24c2f67c2bc2815ba78dba27c0b81e182a1a742b

Observation e1e11723-ef8f-4c6a-9d7e-543e3c62e561 · outbound

This paper cites The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:20.738121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:20.738121Z digest=sha256:7ab650d2eac130b783068bf5676bf25f8c61b31c04e7d084202a63b27c7cec35

Observation 4a866a1a-2ebb-4b71-b40f-8f0fc3445046 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:20.906985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:20.906985Z digest=sha256:afc514558d81e26adb2174da6ab814768553d428b653ef441a878ae687250bf3

Observation 089a9990-faaa-445a-af69-17768580aa9f · outbound

This paper cites Large language models for financial aid in financial time-series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Large language models for financial aid in financial time-series forecasting,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:21.101390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:21.101390Z digest=sha256:66bc4357bbbeb7460b6e77e4702f6f8745c108537b5b961c49c3f2d5b092b150

Observation 5dd4f540-e0cf-4865-bff8-94d409a50456 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A decoder-only foundation model for time-series forecasting

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:21.255306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:21.255306Z digest=sha256:112ac4e0729f713342338ad2a4b7ba567d0b1de2c0313783ba5ee2831639521b

Observation 69dcd7e4-c933-43bd-9849-7dbdb3755734 · outbound

This paper cites Financial Fine-tuning a Large Time Series Model.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Financial Fine-tuning a Large Time Series Model

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:21.433599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:21.433599Z digest=sha256:3995e587e3401e49769d3e472354ce2131c83ebaaa6b8564f561439542adf800

Observation dfb02f44-20c1-43a5-a35d-2d0d592bc564 · outbound

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:21.638310Z digest=sha256:ab9a1664b485d6b386c96ee55dd5d3ab84bba2bd53b01cab6d548fbc5472a371

Observation 860e1601-cf33-43fe-ad2a-838ef791411d · outbound

This paper cites Anomaly detection for vietnamese financial market,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Anomaly detection for vietnamese financial market,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:21.822072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:21.822072Z digest=sha256:f402753376cbeae2dc19188a350734bc6c3b0b300ea3da43023c185456b178c9

Observation 32c3abbd-b517-48a5-973f-7684d38959d1 · outbound

This paper cites Time Series Data Augmentation for Deep Learning: A Survey.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Time Series Data Augmentation for Deep Learning: A Survey

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:22.032092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:22.032092Z digest=sha256:735f981e02cc68b36a818e06d15d860f1531ec7d58c29a8fec9a2a8008b42acb

Observation d56098ba-5fa9-49f5-bcf5-eed3dbfe1496 · outbound

This paper cites Predicting extreme financial risks on imbalanced dataset: A combined kernel fcm and kernel smote based svm classifier,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Predicting extreme financial risks on imbalanced dataset: A combined kernel fcm and kernel smote based svm classifier,

Reference 45

Resolution
verified exact
doi, observed 2026-08-06T18:49:34.484690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:22.209287Z digest=sha256:652d860e3a18ab2fd3f94f38f6c82f667feab82ff549eb8104b0da6e7aa2eb4c

Observation 2ef7f78a-1ebc-4df9-b4bb-f7a4caf6d4d6 · outbound

This paper cites Learning with imbalanced data in smart manufacturing: A comparative study,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Learning with imbalanced data in smart manufacturing: A comparative study,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:22.390568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:22.390568Z digest=sha256:124b2409e67d0ac2bbc6fe28a02c8fa0b47297cd427de02e032cb035534448c7

Observation 3dc86b67-3b93-40c0-804f-82ca836ff134 · outbound

This paper cites A deep learning based expert framework for portfolio prediction and forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A deep learning based expert framework for portfolio prediction and forecasting,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:22.546184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:22.546184Z digest=sha256:affcf53c338c531b9943c991fbb191555427056eb7e10f579317fbb70d8fc109

Observation 62d3bc75-4437-4117-be93-2e076e058d6f · outbound

This paper cites Transfer learning for class imbalance problems with inadequate data,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Transfer learning for class imbalance problems with inadequate data,

Reference 48

Resolution
malformed identifier
no resolver link, observed 2026-08-06T18:49:22.761216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:22.761216Z digest=sha256:1bbf538238789db998bfc9afd639c2f638c342ac0278569c5937d827dfc83a4d

Observation 72941a42-72c6-4913-868b-de71ed666734 · outbound

This paper cites A survey on transfer learning,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A survey on transfer learning,

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:22.950270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:22.950270Z digest=sha256:ecefc2c141df9e22d812dd27a2ffafb8b9167691963ac1b0a36fe5778400d95c

Observation d95d9d5b-12e9-4941-9734-e17b217577b8 · outbound

This paper cites A brief review of domain adaptation,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A brief review of domain adaptation,

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:23.152188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:23.152188Z digest=sha256:8bf3877a87991bbd68943baf68fa165e65e24513d4dec260806477382d9a7090

Observation 1d768b55-8384-4227-8f77-25e527c365ed · outbound

This paper cites A novel deep transfer learning framework with adversarial domain adaptation: Application to financial time-series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A novel deep transfer learning framework with adversarial domain adaptation: Application to financial time-series forecasting,

Reference 51

Resolution
verified exact
doi, observed 2026-08-06T18:49:34.288842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:23.289486Z digest=sha256:4ba1131fe0d499f7247708d463072182b9b64457bbe554851fda946c93399f6c

Observation 50ebe2c6-9892-4347-b176-1771797074a0 · outbound

This paper cites TimeGPT-1.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting TimeGPT-1

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:23.421044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:23.421044Z digest=sha256:949f30a46ea1923db43ec972489e38cbcb5fb9a56afc7a816db77bc56b269ce6

Observation 515645ba-7170-494a-8915-af5c94c80ca2 · outbound

This paper cites Toward a Foundation Model for Time Series Data.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Toward a Foundation Model for Time Series Data

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:49:34.092211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:23.554996Z digest=sha256:f075829a50e4383906bb8201443b650cc1adac7a87d30061f90aa39dbaa46ec6

Observation da7b0e0d-2bdd-4036-b1b2-fb96d0deff30 · outbound

This paper cites Long short-term memory,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Long short-term memory,

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:23.657974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:23.657974Z digest=sha256:16319801f58d9fdf9c9daea7d0a4506b84f3aa3b00e2117e2baa0385cc5de608

Observation 0105246b-bffd-4c0e-9c11-97c2dbd83ee5 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Deep Residual Learning for Image Recognition

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:23.802812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:23.802812Z digest=sha256:4a0330c563381ebb2ff8e8ce96b65dc94ff8c78ecc0e2d6fbcb21ba3ad327dbe

Observation cf7699f6-aa92-4eb0-84bc-2861c575ae8e · outbound

This paper cites Deep learning for time series classification: A review,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Deep learning for time series classification: A review,

Reference 56

Resolution
malformed identifier
no resolver link, observed 2026-08-06T18:49:23.943244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:23.943244Z digest=sha256:7e446016dbd9fe82cb3b59ebfb347b503e7b4b88efed81a48e491323256aba2f

Observation 9dba3374-9b5e-41c1-ac12-f0d23f7e874e · outbound

This paper cites Temporal fusion transformers for interpretable multi- horizon time series forecasting,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Temporal fusion transformers for interpretable multi- horizon time series forecasting,

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.083887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.083887Z digest=sha256:bee62408d37dedb27424fa6d5deff489a1618ea96547d07d8350d035ebed67b1

Observation 14186e64-dc8f-4421-b13a-3236c398d890 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.233714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.233714Z digest=sha256:aed297e33581a425bc05559387df6a26a70dc82c5352494e3b6f1fac4243ffd5

Observation 9b31582e-d6f8-4831-a4f4-a39030f2e9ea · outbound

This paper cites Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.409801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.409801Z digest=sha256:c373d87fe969360ec50966c30b70eb92e2da25fbb6021d47a084b9d6cdabfee2

Observation 1f3ed4c6-cda2-46c1-adf1-846d7ffb70df · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.491230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.491230Z digest=sha256:8b4ad249d1493395f36e2202f450352ea6228a01dcd979199057a7c9779684c7

Observation 479a8710-cf98-4e40-a316-a3fb6b439c14 · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.670918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.670918Z digest=sha256:126c80c165da959fab656b0f4f38cea0fd1ea6becccf600fa78e701beb584726

Observation b0d231e5-fce0-4317-a412-7c414355b513 · outbound

This paper cites Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.844691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.844691Z digest=sha256:b88ce9ba30f64e7eeccdda65f5afbac2238d60f03fee7d939608c3fc702aa0ee

Observation 076fdc1c-b5cb-4ec8-bf94-1468b3e78897 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:24.995738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:24.995738Z digest=sha256:3ae702249d7f39e6f465bd1d8350749b5e50e7b4172a256ae10df6a4e5a67cbf

Observation d38858ab-7787-4035-9913-1af5e6e675d3 · outbound

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

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Chronos: Learning the Language of Time Series

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:25.216351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:25.216351Z digest=sha256:7a9262035a135cf55bbc3d08fba131e3b72970dddc85045a80e3a96c6a442dc0

Observation 3244120a-bef8-4330-8082-f92d4fe72718 · outbound

This paper cites Moment: A family of open time-series foundation models,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Moment: A family of open time-series foundation models,

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:25.366725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:25.366725Z digest=sha256:93c4b9a985285747d79ef320ef2d202f914044d5ad638d12c1203119bebe4340

Observation 10f186c0-80fd-4127-950b-28c95ee1f928 · outbound

This paper cites an unresolved cited work.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unresolved cited work

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:25.547604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:25.547604Z digest=sha256:206220456274ef39b919b0f8153df1520fb9b20a04f329734644964c0ee5e1e4

Observation 0372d93c-a3bf-4537-be03-11b1c195e63c · outbound

This paper cites Full Parameter Fine-tuning for Large Language Models with Limited Resources.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Full Parameter Fine-tuning for Large Language Models with Limited Resources

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:25.726546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:25.726546Z digest=sha256:09fd5f7cb1d64b4debc1cb1748dbe4371bb1d1b8081458d8551edd97fe893214

Observation b9596d81-af4b-4be6-b67f-4a788950bc15 · outbound

This paper cites How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:25.873848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:25.873848Z digest=sha256:331f8bd58f7195655452ec8a3607df6103a987836c2c70f74ed3f777524f1669

Observation 3421a396-4e4e-4763-a549-8c5324ffc215 · outbound

This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.011700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.011700Z digest=sha256:894416f3a3f6d0c9eac18ce35143ed3eb14380aa02cec73ceca762fedf4086af

Observation f60caee2-ee78-4346-88ab-164b5600d80f · outbound

This paper cites Adik, PEFT (Parameter-Efficient Fine-Tuning), https://medium.com/@kanikaadik07 /peft-parameter-efficient-fine-tuning-55e32c60c799 , Accessed: 2025-04-12, 2023.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Adik, PEFT (Parameter-Efficient Fine-Tuning), https://medium.com/@kanikaadik07 /peft-parameter-efficient-fine-tuning-55e32c60c799 , Accessed: 2025-04-12, 2023

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.083108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.083108Z digest=sha256:fa634704041ba8dddc44743b6e656d0916246a000083fd59a9f68b785007ae6c

Observation 81d8f5f0-9cd1-4499-a20c-5c26e9ab173b · outbound

This paper cites DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.221820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.221820Z digest=sha256:1477a3b568848b68f53efe45ebd090333551a884cf8cc4d8005472b3925fe678

Observation 8be7a0bd-4b4f-482e-a69f-d4e0b0ac0054 · outbound

This paper cites an unresolved cited work.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unresolved cited work

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.374181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.374181Z digest=sha256:b0ed3226d2ed77a6988d6194a60667136aff6335234f9ba5c6e75220c03da6fa

Observation 4a33112b-6bd4-4033-a091-ec1427ed36c9 · outbound

This paper cites Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.576798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.576798Z digest=sha256:1a893ba60b64e67617fb9b3d44765a966484211100394b9449fd9a9d27f343bd

Observation 86b4772f-2bc3-4b18-afe3-b32a6fc8e4b5 · outbound

This paper cites Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.708693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.708693Z digest=sha256:8fa5f6a0eedc727b27fe6ddd9c308ac236202954e61daf3fd1d32b474c19c365

Observation cdc065fe-2be7-41ce-96b7-babb0bb4616f · outbound

This paper cites an unresolved cited work.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unresolved cited work

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:26.890209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:26.890209Z digest=sha256:37bb4d9f8d2789875e121743beacde4054f4c803adfc21346f79558c2689a6f4

Observation 37bfc647-31c5-4390-a389-0a0dc140b1ba · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.233404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.233404Z digest=sha256:d320ec10be583f217695cab7df7a20ec268e6a413c31c470f6dcda33366b31d3

Observation 7d1d5520-a198-4334-a3bf-38c7ccb9e686 · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.406346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.406346Z digest=sha256:7b901228f2c6314ccd6797fb1deda9462c99172c087691eb723fd3757e002997

Observation aa5936f1-1b9a-485c-974b-1e9246d1ef5e · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Parameter-Efficient Transfer Learning for NLP

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.558773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.558773Z digest=sha256:a09d9a80ed3f60832fc86fa518d0774f2b2f6d38d579854260aa4ff90b9c3ece

Observation 6a61d21b-7fa4-4d0b-827b-1364b58130aa · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.724947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.724947Z digest=sha256:35ae058611b4a1bbb4b09e21f07b819f5e805d240573355e5bf4f85d4162833d

Observation bbe51cfe-e54f-4c26-a3f8-10b0d426c48e · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Measuring the Intrinsic Dimension of Objective Landscapes

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.061729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.061729Z digest=sha256:6fa631cf1d54144c05464d998c8b14d95f2d885ff0f06a311a533d17b3fd026a

Observation 7a2649be-c4d3-45ab-8679-f089d4a1c96c · outbound

This paper cites SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters

Reference 81

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:49:33.679789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:28.139091Z digest=sha256:9f0edd08690f6c31234589e65ff4c87ecebabaefa3c825ffb407ec701b1c66a3

Observation 2c81956c-acca-4250-846e-104b859ba630 · outbound

This paper cites an unresolved cited work.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Unresolved cited work

Reference 82

Resolution
verified exact
doi, observed 2026-08-06T18:49:33.501894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:28.280507Z digest=sha256:5b671905713fdacabaacb7739b2cbf98fb386d491cb9430eec829d16c10b1aae

Observation de0fd297-bf2b-4d0e-adf7-0e9228c2c7e6 · outbound

This paper cites A review on transferability estimation in deep transfer learning,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A review on transferability estimation in deep transfer learning,

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:28.572625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:28.572625Z digest=sha256:87385f88e128877e761edab3eda960fb983bc14c07754f2ead00f2a55cde0861

Observation 00e215f6-3ac3-4199-8459-a26d8f526b68 · outbound

This paper cites A cointegration analysis of treasury bill yields,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A cointegration analysis of treasury bill yields,

Reference 84

Resolution
verified exact
doi, observed 2026-08-06T18:49:33.360521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:28.674885Z digest=sha256:7ccc3bb052c84b8882b2eb78d4cdefbc501a26a7989e6c5cc6d0bdaeb7ab871b

Observation 647005cc-9d03-4973-a8c6-fce0897bdb38 · outbound

This paper cites Counter-Interference Adapter for Multilingual Machine Translation.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Counter-Interference Adapter for Multilingual Machine Translation

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:27.951946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:27.951946Z digest=sha256:5e7d216f9881ac08bd50d79d1b53117bf4df44cbb0b423dd4da2652fc4693f98

Observation d20f5e0d-e970-4da2-9046-3d23803caaf6 · outbound

This paper cites A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables,

Reference 86

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:28.926330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:28.926330Z digest=sha256:35d3659c62f5bd4f3d961f4529e5b510535adc1e3afeca2ff097805d292a76a0

Observation 85acab2e-4add-4557-b693-f75e86e65126 · outbound

This paper cites Price forecast of treasury bond market yield: Optimize method based on deep learning model,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Price forecast of treasury bond market yield: Optimize method based on deep learning model,

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:29.013607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:29.013607Z digest=sha256:0386b91060a27a8b0558d945a12ccdd1fc7c5f30b2b99827e81898317005f8a4

Observation 5fa119f3-bd7d-47df-b8bd-38490191b742 · outbound

This paper cites GPT Understands, Too.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting GPT Understands, Too

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:28.433587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:28.433587Z digest=sha256:a7a037505642f81f13efb4862f658d687797cde37d515cbc9568bb4db7b3184b

Observation decec7cf-3bd4-424e-9523-6e6f3f51b2a0 · outbound

This paper cites Department of the Treasury, Daily treasury par yield curve rates, 2025.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Department of the Treasury, Daily treasury par yield curve rates, 2025

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:29.218311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:29.218311Z digest=sha256:cb4e414472b5a43c6a0e29bceef037d3f0b4e0f41941d784daf27b2e3a2aeb60

Observation 38d5c0b2-1ea3-4222-8014-7d70e6509ae3 · outbound

This paper cites Louis, Federal reserve economic data (fred), Accessed: 2025-05-22,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Louis, Federal reserve economic data (fred), Accessed: 2025-05-22,

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:29.340891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:29.340891Z digest=sha256:778f681a51a9a2d335f6fae6ba85de28ac61a7c329c96e89ebd9065eb7a07660

Observation 3f5a1065-8718-4f32-84ac-c566c42a6cda · outbound

This paper cites Forecasting interest rates,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Forecasting interest rates,

Reference 91

Resolution
verified exact
doi, observed 2026-08-06T18:49:33.177994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:28.796199Z digest=sha256:31cdb33a7cf716bc9c5d0ca8863f958707123b75be17aa85c976a18671d4d8d7

Observation 49a08524-7bd2-477d-9911-6c4b5e26bc09 · outbound

This paper cites Forecasting volatility in financial markets: A review,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Forecasting volatility in financial markets: A review,

Reference 92

Resolution
malformed identifier
doi_truncated, observed 2026-08-06T18:49:32.983709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:29.774627Z digest=sha256:a99cf68f6987d9d9a1023112d41290b542cf814f25f5c5a9d1380808c234c71c

Observation 67398cc8-ad85-42f4-9176-1b43740fea04 · outbound

This paper cites Chapter 49 arch models,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Chapter 49 arch models,

Reference 93

Resolution
verified exact
doi, observed 2026-08-06T18:49:32.814207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:29.910562Z digest=sha256:32d6e5e9f3b8818b1011296056fc8aefc5fb2c1543646abbfbf946a868bc8c5e

Observation 3e6f7ed2-0d8d-4e0c-b2a0-835c9892b837 · outbound

This paper cites Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:29.132064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:29.132064Z digest=sha256:48407ae369b57280a44e3d07f4c0569ea87cc72fd0db385a22def8333dabe797

Observation e546a612-52cd-49c0-882a-cc7eaaa735af · outbound

This paper cites The volatility of realized volatility,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting The volatility of realized volatility,

Reference 95

Resolution
verified exact
doi, observed 2026-08-06T18:49:32.654404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:30.111814Z digest=sha256:a8d2711d18bd3c51793773149b3c2591a79c839db27d8f6f9b3bcde7cbdef8b5

Observation 50751144-dd47-4b62-b501-c44fae960f49 · outbound

This paper cites Realized volatility forecasting with neural networks,.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Realized volatility forecasting with neural networks,

Reference 96

Resolution
verified exact
doi, observed 2026-08-06T18:49:32.481782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:30.250113Z digest=sha256:33633de158f9f8f8b4a745022b1fe80ac2949f5ace149e82ff93e710a924a295

Observation c5d22e78-32b8-4f0e-ad8f-c43f6f16ff12 · outbound

This paper cites A forecast comparison of volatility models: Does anything beat a garch(1, 1)?.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting A forecast comparison of volatility models: Does anything beat a garch(1, 1)?

Reference 97

Resolution
verified exact
doi, observed 2026-08-06T18:49:32.317970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:49:30.410843Z digest=sha256:a236ca48101b9a55259a04d350960812084ae227baa50c7269a54043abb4dec8

Observation b11aad49-d41e-4589-9d3a-5fd631f429be · outbound

This paper cites Ltd., Quantamental indicators on jpmaqs, Accessed: 2025-05-22, 2025.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Ltd., Quantamental indicators on jpmaqs, Accessed: 2025-05-22, 2025

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:29.570915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:29.570915Z digest=sha256:3c05a959c2d265216187069948697ba30306d3cc723401cc84043d919a20502a

Observation 90a90729-5a05-451e-bd4c-872f4add2306 · outbound

This paper cites [Online].

Time Series Foundation Models for Multivariate Financial Time Series Forecasting [Online]

Reference 99

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:30.803632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:30.803632Z digest=sha256:d106783ba14464e9e3f371d4bac7cf249e45b087efd36cbd3da480b1738b1e57

Observation 9c788ad2-baa3-45da-be26-06290da07e12 · outbound

This paper cites [Online].

Time Series Foundation Models for Multivariate Financial Time Series Forecasting [Online]

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-06T18:49:30.906268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:49:30.906268Z digest=sha256:f6909ea53d6cf197b13eb5133a2b02c4ffe9ced2a8494dbc04c01e75b6f9faf2

Pith citing papers

Observation 03a57544-24d9-43ae-9147-4e12f66aa39b · inbound

Towards Causal Market Simulators cites this paper.

Towards Causal Market Simulators Time Series Foundation Models for Multivariate Financial Time Series Forecasting

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-03T23:43:05.341575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:43:05.341575Z digest=sha256:c07bd7ccf2fe9bfd0b398a18c82733b69c750e75517053cdc08e7003a0cf3e73

Observation c1567ad0-19b3-412a-994b-44376fe06a2e · inbound

Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks cites this paper.

Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks Time Series Foundation Models for Multivariate Financial Time Series Forecasting

Reference 94

Resolution
verified exact
local_arxiv, observed 2026-07-07T19:34:06.504243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-07T19:31:46.593904Z digest=sha256:a5ec7305cf2f63dca6a04f6ac17ede70bbacd81541b48183e9faec5f44cf73db