Paper Citation Record · LEDGER

Foundation Time-Series AI Model for Realized Volatility Forecasting

As of 17 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2505.11163.

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

pith.paper-citation-record.v1
2505.11163 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

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measured 59 of 59 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-07T19:31:46.593904Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T19:34:06.417174Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74b740e9-1315-4086-9cd5-16d9f88c5de3 · outbound

This paper cites Andersen, Tim Bollerslev, and Francis X.

Foundation Time-Series AI Model for Realized Volatility Forecasting Andersen, Tim Bollerslev, and Francis X

Reference 1

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Observation f84a9086-837e-4421-84ff-d34c636a2f7e · outbound

This paper cites Chronos: Learning the language of time series.

Foundation Time-Series AI Model for Realized Volatility Forecasting Chronos: Learning the language of time series

Reference 2

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Observation 67595f74-12f8-4a69-b50b-dc18b89aafd4 · outbound

This paper cites Garch based artificial neural networks in forecasting conditional variance of stock returns.

Foundation Time-Series AI Model for Realized Volatility Forecasting Garch based artificial neural networks in forecasting conditional variance of stock returns

Reference 3

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Observation 827a0493-0f10-41cc-b804-7e15582b66a0 · outbound

This paper cites Lassoing the har model: A model selection perspective on realized volatility dynamics.

Foundation Time-Series AI Model for Realized Volatility Forecasting Lassoing the har model: A model selection perspective on realized volatility dynamics

Reference 4

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Observation 3c13c5af-da28-4b57-a945-676589c3245e · outbound

This paper cites The impact of senti- ment and attention measures on stock market volatility.

Foundation Time-Series AI Model for Realized Volatility Forecasting The impact of senti- ment and attention measures on stock market volatility

Reference 5

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Observation 524e93d4-3a4e-4bb4-a554-35ad0bb4cf04 · outbound

This paper cites Estimating quadratic variation using realized variance.

Foundation Time-Series AI Model for Realized Volatility Forecasting Estimating quadratic variation using realized variance

Reference 6

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Observation 98fe2f19-959e-4cd7-bdad-b7b3c364b64b · outbound

This paper cites Barndorff-Nielsen and Neil Shephard.

Foundation Time-Series AI Model for Realized Volatility Forecasting Barndorff-Nielsen and Neil Shephard

Reference 7

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Observation 0cedf8ab-b521-42a5-a9cf-4cfc95b82005 · outbound

This paper cites Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models.

Foundation Time-Series AI Model for Realized Volatility Forecasting Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models

Reference 8

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Observation e006e05b-be4d-4e73-9180-6f1f83712082 · outbound

This paper cites Generalized autoregressive conditional heteroskedasticity.

Foundation Time-Series AI Model for Realized Volatility Forecasting Generalized autoregressive conditional heteroskedasticity

Reference 9

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Observation 73906c12-e215-49ce-9a79-478072abfd52 · outbound

This paper cites Patton, and Rogier Quaedvlieg.

Foundation Time-Series AI Model for Realized Volatility Forecasting Patton, and Rogier Quaedvlieg

Reference 10

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Observation 5ae995a7-a83b-4461-b522-faa0736c1836 · outbound

This paper cites Realized volatility forecasting with neural networks.

Foundation Time-Series AI Model for Realized Volatility Forecasting Realized volatility forecasting with neural networks

Reference 11

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Observation aecd941d-a198-4d53-9175-261aff664934 · outbound

This paper cites TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model.

Foundation Time-Series AI Model for Realized Volatility Forecasting TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model

Reference 12

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Observation ffc69efd-1403-4124-8f07-2105895f28bb · outbound

This paper cites Building news measures from textual data and an application to volatility forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Building news measures from textual data and an application to volatility forecasting

Reference 13

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Observation 7b44141e-6a21-49a8-9f79-acb1481ced3a · outbound

This paper cites VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters.

Foundation Time-Series AI Model for Realized Volatility Forecasting VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 14

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Observation 51746b54-15c5-4817-a15f-b3c4671dab73 · outbound

This paper cites A machine learn- ing approach to volatility forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting A machine learn- ing approach to volatility forecasting

Reference 15

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Observation 9d94e21f-7e7e-435d-a207-5c3b99b44b2b · outbound

This paper cites A simple approximate long-memory model of realized volatility.

Foundation Time-Series AI Model for Realized Volatility Forecasting A simple approximate long-memory model of realized volatility

Reference 16

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Observation 1bea16a6-2c0a-45cb-a9b8-1d8775d5ace0 · outbound

This paper cites Har volatility modelling with heterogeneous leverage and jumps.

Foundation Time-Series AI Model for Realized Volatility Forecasting Har volatility modelling with heterogeneous leverage and jumps

Reference 17

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Observation 7c3fe832-1beb-4d1a-90e7-b9ad9c3dbc12 · outbound

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

Foundation Time-Series AI Model for Realized Volatility Forecasting A decoder- only foundation model for time-series forecasting

Reference 18

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Observation 9a8ff9fc-1f06-497e-9e17-dc15c46f54ed · outbound

This paper cites Diebold and Robert S.

Foundation Time-Series AI Model for Realized Volatility Forecasting Diebold and Robert S

Reference 19

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Observation f52b13fb-5e37-400a-873e-35ac2c5ce2a8 · outbound

This paper cites Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series.

Foundation Time-Series AI Model for Realized Volatility Forecasting Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series

Reference 20

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Observation f0a75cb4-d357-4e3d-8f93-57acb37d990a · outbound

This paper cites Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation.

Foundation Time-Series AI Model for Realized Volatility Forecasting Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation

Reference 21

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Observation c06deaa6-ac81-4754-a5dc-2674665aa9af · outbound

This paper cites Modeling and predicting the cboe market volatility index.

Foundation Time-Series AI Model for Realized Volatility Forecasting Modeling and predicting the cboe market volatility index

Reference 22

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This paper cites Using lstm and gru neural network methods for traffic flow prediction.

Foundation Time-Series AI Model for Realized Volatility Forecasting Using lstm and gru neural network methods for traffic flow prediction

Reference 23

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Observation 0dae225a-bfa2-453e-9367-6fc419a72aab · outbound

This paper cites Neural network–based financial volatility forecasting: A systematic review.

Foundation Time-Series AI Model for Realized Volatility Forecasting Neural network–based financial volatility forecasting: A systematic review

Reference 24

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Observation ce9d6e51-c228-4a61-a681-803cd2f5363e · outbound

This paper cites Tests of conditional predictive ability.

Foundation Time-Series AI Model for Realized Volatility Forecasting Tests of conditional predictive ability

Reference 25

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Observation 5f86bb7d-4ddb-454a-a029-38349050f0c6 · outbound

This paper cites Monash Time Series Forecasting Archive.

Foundation Time-Series AI Model for Realized Volatility Forecasting Monash Time Series Forecasting Archive

Reference 26

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Observation 442a95ba-7112-426d-b850-9d87a3bfabc5 · outbound

This paper cites Time-Series Foundation AI Model for Value-at-Risk Forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Time-Series Foundation AI Model for Value-at-Risk Forecasting

Reference 27

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Observation 89ef30f5-02da-497b-80cd-b13c3f1ed619 · outbound

This paper cites Using neural networks for forecasting volatility of s&p 500 index futures prices.

Foundation Time-Series AI Model for Realized Volatility Forecasting Using neural networks for forecasting volatility of s&p 500 index futures prices

Reference 28

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Observation 8dc25293-f916-4554-902c-e5b6a629083d · outbound

This paper cites Hansen and Asger Lunde.

Foundation Time-Series AI Model for Realized Volatility Forecasting Hansen and Asger Lunde

Reference 29

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Observation 0d978524-5a0e-48f8-ad68-10d333492e4f · outbound

This paper cites The model confidence set.

Foundation Time-Series AI Model for Realized Volatility Forecasting The model confidence set

Reference 30

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Observation 7533840b-9ddb-4d08-8dbd-3df6497c9133 · outbound

This paper cites Realized garch: a joint model for returns and realized measures of volatility.

Foundation Time-Series AI Model for Realized Volatility Forecasting Realized garch: a joint model for returns and realized measures of volatility

Reference 31

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Observation 1c68f376-3c9c-47f5-a628-07f644849f30 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Foundation Time-Series AI Model for Realized Volatility Forecasting Masked autoencoders are scalable vision learners

Reference 32

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Observation d3973657-53ee-4e04-a47d-15426e840ad1 · outbound

This paper cites Combining conditional volatility forecasts using neural networks: an application to the ems exchange rates.

Foundation Time-Series AI Model for Realized Volatility Forecasting Combining conditional volatility forecasts using neural networks: an application to the ems exchange rates

Reference 33

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Observation 00ea06f9-3291-4d4a-9ecf-7a43cbc5cb8a · outbound

This paper cites Fore- casting realised volatility using arfima and har models.

Foundation Time-Series AI Model for Realized Volatility Forecasting Fore- casting realised volatility using arfima and har models

Reference 34

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Observation b15e1e1d-c4d6-4561-a03e-bf07cda65843 · outbound

This paper cites Volatility fore- cast using hybrid neural network models.

Foundation Time-Series AI Model for Realized Volatility Forecasting Volatility fore- cast using hybrid neural network models

Reference 35

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Observation 9aeeb382-523f-437c-a097-d738d073f09b · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Foundation Time-Series AI Model for Realized Volatility Forecasting Foundation models for time series analysis: A tutorial and survey

Reference 36

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Observation e129ea56-e203-4204-9eb5-6bdeb20d8a3a · outbound

This paper cites Trading volume and realized volatility forecasting: Evidence from the china stock market.

Foundation Time-Series AI Model for Realized Volatility Forecasting Trading volume and realized volatility forecasting: Evidence from the china stock market

Reference 37

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Observation 053c97eb-a93c-4f5f-89ae-2ee6bcc4a43a · outbound

This paper cites Novel volatility forecasting using deep learning–long short term memory recurrent neural networks.

Foundation Time-Series AI Model for Realized Volatility Forecasting Novel volatility forecasting using deep learning–long short term memory recurrent neural networks

Reference 38

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Observation 413201c7-16c3-4491-bdd6-77bef8019913 · outbound

This paper cites Forecasting of realised volatility with the random forests algorithm.

Foundation Time-Series AI Model for Realized Volatility Forecasting Forecasting of realised volatility with the random forests algorithm

Reference 39

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Observation 458ccc60-a6e5-4d91-b307-6c727d594f53 · outbound

This paper cites The m4 competition: 100,000 time series and 61 forecasting methods.

Foundation Time-Series AI Model for Realized Volatility Forecasting The m4 competition: 100,000 time series and 61 forecasting methods

Reference 40

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Observation ada1635f-ed27-4a26-817d-c0b89cc97a27 · outbound

This paper cites Stock market volatil- ity: Identifying major drivers and the nature of their impact.

Foundation Time-Series AI Model for Realized Volatility Forecasting Stock market volatil- ity: Identifying major drivers and the nature of their impact

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.478705Z

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Observation 1a238189-1cee-452b-bbbb-0e474a6b8ded · outbound

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

Foundation Time-Series AI Model for Realized Volatility Forecasting A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 42

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This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:42.151646Z

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This paper cites Deep adaptive input normalization for time series forecast- ing.

Foundation Time-Series AI Model for Realized Volatility Forecasting Deep adaptive input normalization for time series forecast- ing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.466070Z

Source-reported events for the cited work

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Observation 006584b2-92a0-4684-b3fb-437cb0a33a55 · outbound

This paper cites Volatility forecast comparison using imperfect volatility proxies.

Foundation Time-Series AI Model for Realized Volatility Forecasting Volatility forecast comparison using imperfect volatility proxies

Reference 45

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Observation 80c567e1-9def-4c08-a69a-d7d199f3b956 · outbound

This paper cites Good volatility, bad volatility: Signed jumps and the persistence of volatility.

Foundation Time-Series AI Model for Realized Volatility Forecasting Good volatility, bad volatility: Signed jumps and the persistence of volatility

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.443678Z

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Observation b1049693-bb3c-4c9d-b1e0-dd8f9363ddc7 · outbound

This paper cites A review of general- ized zero-shot learning methods.

Foundation Time-Series AI Model for Realized Volatility Forecasting A review of general- ized zero-shot learning methods

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.433207Z

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Observation feabb73e-a925-41c8-a5d2-820fe01e767e · outbound

This paper cites Machine learning for realised volatility forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Machine learning for realised volatility forecasting

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.422047Z

Source-reported events for the cited work

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Observation 4be5a53e-ed9c-4906-b66a-acd066e7a94b · outbound

This paper cites Lag-llama: Towards foundation models for time series forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Lag-llama: Towards foundation models for time series forecasting

Reference 49

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This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

Foundation Time-Series AI Model for Realized Volatility Forecasting Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:42.176630Z

Source-reported events for the cited work

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Observation 42112988-3eb3-4295-8096-252c33596d1e · outbound

This paper cites Forecasting stock market volatility using realized garch model: International evidence.

Foundation Time-Series AI Model for Realized Volatility Forecasting Forecasting stock market volatility using realized garch model: International evidence

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.398277Z

Source-reported events for the cited work

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Observation 7a5e1fec-736a-44e9-ac11-3dd1e5d22510 · outbound

This paper cites Introducing nbeatsx to realized volatility forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Introducing nbeatsx to realized volatility forecasting

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.386933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca201933-4787-4c7e-9a4b-360ada691ced · outbound

This paper cites Financial returns modelled by the product of two stochastic processes-a study of the daily sugar prices 1961-75.

Foundation Time-Series AI Model for Realized Volatility Forecasting Financial returns modelled by the product of two stochastic processes-a study of the daily sugar prices 1961-75

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.375377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06b7e588-a822-4807-9df9-e0fbb055d914 · outbound

This paper cites Data-driven Neural Architecture Learning For Financial Time-series Forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Data-driven Neural Architecture Learning For Financial Time-series Forecasting

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:03:42.243158Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bc9ad8e3-b2ec-4cee-b5a7-cdc190587f1d · outbound

This paper cites Unified training of universal time series forecasting transformers.

Foundation Time-Series AI Model for Realized Volatility Forecasting Unified training of universal time series forecasting transformers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:42.192643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8178ab-0f14-4e49-91a4-463db26c0503 · outbound

This paper cites Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions.

Foundation Time-Series AI Model for Realized Volatility Forecasting Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.356007Z

Source-reported events for the cited work

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Observation 22ff186a-3eaa-4c63-8eef-d3470ca56e97 · outbound

This paper cites Deeplob: Deep convolutional neural networks for limit order books.

Foundation Time-Series AI Model for Realized Volatility Forecasting Deeplob: Deep convolutional neural networks for limit order books

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.343785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0bb30e27-cd64-4af8-b965-6bc028406379 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time- series forecasting.

Foundation Time-Series AI Model for Realized Volatility Forecasting Informer: Beyond efficient transformer for long sequence time- series forecasting

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:42.331028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 49489ffb-c6de-4fc0-bd4d-8ee211e63430 · 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 Foundation Time-Series AI Model for Realized Volatility Forecasting

Reference 88

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T19:34:06.419352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.