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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.

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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

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External citation measurements

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 29

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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

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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

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This paper cites Baltruˇsaitis, C.

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

Reference 32

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

Reference 75

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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

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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

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source=pdf_text observed=2026-08-06T18:49:27.406346Z digest=sha256:41f97744031944930f742390f07488b92ebb0627f33d2f3b1e30e8b87b55b7cc

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

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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

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source=pdf_text observed=2026-08-06T18:49:27.724947Z digest=sha256:2cf19ac68974caf1b24d9f02ebbd189ffc0cc961095ea23cd99d09bae8b0c805

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

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source=pdf_text observed=2026-08-06T18:49:27.061729Z digest=sha256:bab9db6f4eab651451979e14fc70632d05ad7a6c0c4dc19253137a63c5b3127f

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

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source=pdf_text observed=2026-08-06T18:49:28.139091Z digest=sha256:5ecc86e99df3430474e0e0bd8ebba52048a5e2c78876ba71e708ff48db5b9b44

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

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source=pdf_text observed=2026-08-06T18:49:28.280507Z digest=sha256:19f8881d5a7f2b3b7df8be5051bd3475b5022a29bb354094e7bf63eb855afbc2

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

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source=pdf_text observed=2026-08-06T18:49:28.572625Z digest=sha256:6747dd4613d325b116589a268c642761740fba210c5e7a8862afccdc4d14214b

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

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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=sha256:60df4104d2fa69f60a7d0dbd68ae39338aaabd09530214f8e07196d6d6b3e035

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

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source=pdf_text observed=2026-08-06T18:49:27.951946Z digest=sha256:0030cf408cbfd8db020afea532c94dd3828d0137c0f8296858321f5daa40c6e9

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

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source=pdf_text observed=2026-08-06T18:49:28.926330Z digest=sha256:264dc17e27ad67687643100433a400e404812525a5965aa19d7af2156ef67477

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

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source=pdf_text observed=2026-08-06T18:49:29.013607Z digest=sha256:062ba81fc709c98514f93c026d9d8be07d1e3ea4f5f474dd02a806b85eb130d6

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

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source=pdf_text observed=2026-08-06T18:49:28.433587Z digest=sha256:c0e471a1bc2b22d6e469963cd97167a1f05a8cb04afb7351d64b5963572332b1

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

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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

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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

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source=pdf_text observed=2026-08-06T18:49:28.796199Z digest=sha256:de5269fb7b1d62b6b7fed192d3b5bb56759bf5f12df0b287519bd500505f61f3

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

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source=pdf_text observed=2026-08-06T18:49:29.774627Z digest=sha256:e80a73b0545268ee24b05ab546f27839b0be90b7246fcd015e5cd167b2c2b46a

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

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source=pdf_text observed=2026-08-06T18:49:29.910562Z digest=sha256:e39e7f7900f54e213e946f693e85ebc18d091f2db71ae1e4d6b42642c6e06116

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

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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

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source=pdf_text observed=2026-08-06T18:49:30.111814Z digest=sha256:b9c4201592645174314c2751dedb34df97e81a6e1cc4aae7df74aa1fe04571f4

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

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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

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source=pdf_text observed=2026-08-06T18:49:30.410843Z digest=sha256:02552d9bfc1e48308ad97481f958272c44f7561002bbbbbca336a35a394f01d5

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

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

This paper cites [Online].

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

Reference 99

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

This paper cites [Online].

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

Reference 100

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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

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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

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source=arxiv_source observed=2026-07-07T19:31:46.593904Z digest=sha256:4b5adccea9a47a9e3931ad5be0702bffd7d54350cd1e878f6cde85a32fc3bfd9