Paper Citation Record · LEDGER

Deep learning of point processes for modeling high-frequency data

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2504.15944.

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

pith.paper-citation-record.v1
2504.15944 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:19:59.871038Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact3
verified fuzzy20
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23967ea2-5f1a-486e-9fb6-d0a61236cdeb · outbound

This paper cites application to high frequency financial data.

Deep learning of point processes for modeling high-frequency data application to high frequency financial data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.245522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1239bf42-9122-4387-b8c3-ac9d386956e4 · outbound

This paper cites Quantitative Finance 13(1), 65–77 (2013).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 13(1), 65–77 (2013)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.235170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c04c3991-5299-4340-a1d4-54846fe830c8 · outbound

This paper cites Journal of Econometrics 141(2), 876–912 (2007).

Deep learning of point processes for modeling high-frequency data Journal of Econometrics 141(2), 876–912 (2007)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.224237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77c1f9f3-f92c-4e85-9fbe-21ad5e9bcabd · outbound

This paper cites Acta Numerica 30, 327 – 444 (2021).

Deep learning of point processes for modeling high-frequency data Acta Numerica 30, 327 – 444 (2021)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.773860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 691f4ce8-1697-4d62-9437-6b46d051bd94 · outbound

This paper cites Statistical Science 36(2) (2021).

Deep learning of point processes for modeling high-frequency data Statistical Science 36(2) (2021)

Reference 5

Resolution: verified exact; doi, observed 2026-08-16T11:19:59.973477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3df7c54c-40e7-41a3-881d-8f97d1eae091 · outbound

This paper cites Econometrica 89(1), 181 – 213 (2021).

Deep learning of point processes for modeling high-frequency data Econometrica 89(1), 181 – 213 (2021)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.781970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bd9405c-f6e4-4b41-8ea7-d2b5ae665a27 · outbound

This paper cites Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training.

Deep learning of point processes for modeling high-frequency data Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.786597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbfb0e70-95b7-4525-8071-ee7345bbba87 · outbound

This paper cites Transformers are Minimax Optimal Nonparametric In-Context Learners.

Deep learning of point processes for modeling high-frequency data Transformers are Minimax Optimal Nonparametric In-Context Learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.790333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae8949e-1f80-4868-8af9-1980830ea6e1 · outbound

This paper cites Bernoulli 31(1) (2025).

Deep learning of point processes for modeling high-frequency data Bernoulli 31(1) (2025)

Reference 9

Resolution: verified exact; doi, observed 2026-08-16T11:19:59.938327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf3f2b05-b25f-494b-95d0-2ab430a79cf0 · outbound

This paper cites Journal of Financial Markets 10(1), 1–25 (2007).

Deep learning of point processes for modeling high-frequency data Journal of Financial Markets 10(1), 1–25 (2007)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.213862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 776521f5-4b3d-48f1-8dde-2d7e36076d4f · outbound

This paper cites Quantitative Finance 18(2), 249–264 (2018).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 18(2), 249–264 (2018)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.203668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3027c651-2c68-4979-99af-2bf8b3e5b5c3 · outbound

This paper cites Quantitative Finance 22(11), 1989–2003 (2022) 33.

Deep learning of point processes for modeling high-frequency data Quantitative Finance 22(11), 1989–2003 (2022) 33

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.193717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8552377a-5671-4c1e-ae61-0862c84e109a · outbound

This paper cites In: High dimensional probability V: the Luminy volume, vol.

Deep learning of point processes for modeling high-frequency data In: High dimensional probability V: the Luminy volume, vol

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.182213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb56da59-df50-46f2-9a52-a46dc1c2c550 · outbound

This paper cites Quantitative Finance 22(3), 563–583 (2022).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 22(3), 563–583 (2022)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.171798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21cb92e6-25f9-4c56-b4e0-822fad2161d6 · outbound

This paper cites Economics discussion paper (2011-32) (2011).

Deep learning of point processes for modeling high-frequency data Economics discussion paper (2011-32) (2011)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.160301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 395cb317-09ea-47d1-b445-991ca277e14e · outbound

This paper cites Quantitative Finance 17(5), 683–701 (2017).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 17(5), 683–701 (2017)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.149375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1536267e-bd9b-4a27-9234-f4836f1ec25a · outbound

This paper cites Quantitative Finance pp.

Deep learning of point processes for modeling high-frequency data Quantitative Finance pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.138628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 48ce1e1c-22a6-48a0-9d8a-b0432acf546b · outbound

This paper cites Japanese Journal of Statistics and Data Science 5(1), 1–39 (2022).

Deep learning of point processes for modeling high-frequency data Japanese Journal of Statistics and Data Science 5(1), 1–39 (2022)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.127547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb3e4e24-9809-4fd0-a8a1-7c5ca8f77a5e · outbound

This paper cites Diffusion Models are Minimax Optimal Distribution Estimators.

Deep learning of point processes for modeling high-frequency data Diffusion Models are Minimax Optimal Distribution Estimators

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.827091Z

Source-reported events for the cited work

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Observation aaa9f6e6-8690-462d-8ddd-6c64bfde0b50 · outbound

This paper cites Quantitative Finance 17(7), 999–1020 (2017).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 17(7), 999–1020 (2017)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.117102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1fab9b52-4b8a-4b6d-aeb2-492b1a4001f4 · outbound

This paper cites Springer (2017).

Deep learning of point processes for modeling high-frequency data Springer (2017)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.106528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a97ab465-a97d-43e3-9340-85d8c2dc7d6a · outbound

This paper cites The Annals of Statistics 48(4), 1875 – 1897 (2020).

Deep learning of point processes for modeling high-frequency data The Annals of Statistics 48(4), 1875 – 1897 (2020)

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.837607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70547830-1625-489b-960c-6b3a88b2c6e7 · outbound

This paper cites Market Microstructure and Liquidity (2023).

Deep learning of point processes for modeling high-frequency data Market Microstructure and Liquidity (2023)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.095390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb6a4b48-386b-4eea-9bd7-2c4160e37580 · outbound

This paper cites SIAM (2009).

Deep learning of point processes for modeling high-frequency data SIAM (2009)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.083939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35cecd3c-7f1d-41e5-9a6d-de9f597a39b2 · outbound

This paper cites Quantitative Finance 19(4), 549–570 (2019).

Deep learning of point processes for modeling high-frequency data Quantitative Finance 19(4), 549–570 (2019)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.847437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0adb49a5-6765-42a3-81fa-b67b2122ea20 · outbound

This paper cites A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models.

Deep learning of point processes for modeling high-frequency data A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:59.850603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3ec0fe7-278c-41b1-bcb2-f469550749eb · outbound

This paper cites Advances in Neural Information Processing Systems 34, 3609–3621 (2021) 34.

Deep learning of point processes for modeling high-frequency data Advances in Neural Information Processing Systems 34, 3609–3621 (2021) 34

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.060422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 575536d7-26e0-46b7-83d1-6f09cbcbff86 · outbound

This paper cites In: 2017 IEEE 19th conference on business informatics (CBI), vol.

Deep learning of point processes for modeling high-frequency data In: 2017 IEEE 19th conference on business informatics (CBI), vol

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.038186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf2f75e0-067f-4848-ad3d-1519979c07c3 · outbound

This paper cites Market microstructure and liquidity (2022).

Deep learning of point processes for modeling high-frequency data Market microstructure and liquidity (2022)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.016718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 671e6b5b-6e50-43ba-8493-5c5d9a1d6506 · outbound

This paper cites an unresolved cited work.

Deep learning of point processes for modeling high-frequency data Unresolved cited work

Reference 30

Resolution: verified exact; doi, observed 2026-08-16T11:19:59.901543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a45c0a1-2f01-4f0c-a4cf-04e47ceabae1 · outbound

This paper cites Annals of the Institute of Statistical Mathematics pp.

Deep learning of point processes for modeling high-frequency data Annals of the Institute of Statistical Mathematics pp

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:20:00.002156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05930ea9-97c0-4cce-80a0-d88adff9b6e9 · outbound

This paper cites an unresolved cited work.

Deep learning of point processes for modeling high-frequency data Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:19:59.990277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.