Paper Citation Record · LEDGER

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2507.07834.

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

pith.paper-citation-record.v1
2507.07834 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:41:05.575004Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:41:01.396830Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:41:05.667096Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy25
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce0d008f-c1d3-4b30-ba63-703ba2a68ecf · outbound

This paper cites Nearing, D.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Nearing, D

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.211122Z

Source-reported events for the cited work

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

Observation bb716745-57c7-4df7-a85e-76e798532cb3 · outbound

This paper cites Chang, Extreme events, economic uncertainty and speculation on occurrences of price bubbles in crude oil futures, Energy Economics130, 107318 (2024).

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Chang, Extreme events, economic uncertainty and speculation on occurrences of price bubbles in crude oil futures, Energy Economics130, 107318 (2024)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.205268Z

Source-reported events for the cited work

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

Observation 877fcf89-7d6d-4ae5-b67d-fe0bc5f920f9 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:09.199432Z

Source-reported events for the cited work

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

Observation 22be60cf-08b2-4e6f-86f1-7afca30ca688 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:09.193331Z

Source-reported events for the cited work

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

Observation c0c6a655-64af-4cbb-81fb-e1978002e8a5 · outbound

This paper cites Pavithran, V.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Pavithran, V

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.187520Z

Source-reported events for the cited work

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

Observation 2606c756-8799-4913-9f2e-b44a01b0fc88 · outbound

This paper cites Durairaj, S.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Durairaj, S

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.181381Z

Source-reported events for the cited work

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

Observation 4692ea95-6615-4b16-86af-22881b60dc10 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:09.175176Z

Source-reported events for the cited work

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

Observation d5bac8ac-005f-4dc1-a3ca-2bfa22014763 · outbound

This paper cites Yuan and A.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Yuan and A

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.168985Z

Source-reported events for the cited work

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

Observation 8b88cd14-2e69-4649-ab31-94f9a129ff1d · outbound

This paper cites Schweigler and J.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Schweigler and J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.163112Z

Source-reported events for the cited work

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

Observation 4388c942-3935-44b8-bdc8-4892108cbc6e · outbound

This paper cites Mehrabbeik, S.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Mehrabbeik, S

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.156093Z

Source-reported events for the cited work

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

Observation 616d89c3-2f61-4aec-b25b-dd7081f29400 · outbound

This paper cites Wang, Using machine learning to analyze the changes in extreme precipitation in southern China, Atmospheric Research 302, 107307 (2024).

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Wang, Using machine learning to analyze the changes in extreme precipitation in southern China, Atmospheric Research 302, 107307 (2024)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.149902Z

Source-reported events for the cited work

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

Observation 32b57336-9adc-4d2c-bff1-3c6a4bad9d49 · outbound

This paper cites Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T18:41:05.749353Z

Source-reported events for the cited work

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

Observation 6f2e0e88-f433-423c-bf7d-656953a3f474 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:09.137242Z

Source-reported events for the cited work

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

Observation 6d4b94f2-9661-4117-a608-15ce9c7849a3 · outbound

This paper cites Teng and L.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Teng and L

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.035149Z

Source-reported events for the cited work

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

Observation 6525190f-08c7-4f27-a75d-4365010efd7e · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:09.143990Z

Source-reported events for the cited work

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

Observation e10fb576-5367-476e-986f-1faa51e0851c · outbound

This paper cites Lellep, J.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Lellep, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.756068Z

Source-reported events for the cited work

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

Observation 333e9a09-e6ac-453d-ba7a-e2ea35d03d80 · outbound

This paper cites Transition.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Transition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.223731Z

Source-reported events for the cited work

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

Observation ca6449ca-d6f8-4f5c-abe5-73dde376a7a1 · outbound

This paper cites Hénon, A two-dimensional mapping with a strange attractor, Communications in Mathematical Physics50, 69 (1976).

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Hénon, A two-dimensional mapping with a strange attractor, Communications in Mathematical Physics50, 69 (1976)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.919713Z

Source-reported events for the cited work

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

Observation 6c3c81ba-6923-4356-b030-f78e69c58ee9 · outbound

This paper cites Setyonegoro, M.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Setyonegoro, M

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.225545Z

Source-reported events for the cited work

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

Observation 9d48df8b-0f70-4b2c-95c4-fb466d57dff2 · outbound

This paper cites Mishra, S.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Mishra, S

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.556147Z

Source-reported events for the cited work

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

Observation 563e1c2b-476e-40b8-9e7e-1ea1e6016aa2 · outbound

This paper cites Tinti, E.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Tinti, E

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.353960Z

Source-reported events for the cited work

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

Observation c6b75a55-3e90-4d49-8c09-37c6bc1fd816 · outbound

This paper cites Kaveh and H.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Kaveh and H

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:07.866126Z

Source-reported events for the cited work

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

Observation 0b267fd2-13ad-4d33-aaeb-e13b843a702b · outbound

This paper cites López-Reyes, On the impact of initial conditions in the forecast of Hurricane Leslie extratropical transition, Atmospheric Research 295, 107020.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques López-Reyes, On the impact of initial conditions in the forecast of Hurricane Leslie extratropical transition, Atmospheric Research 295, 107020

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:08.089857Z

Source-reported events for the cited work

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

Observation 93dff7ae-5f57-4b56-910c-3b0f42a04f09 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:07.980290Z

Source-reported events for the cited work

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

Observation 1787f5f3-3d18-4614-8ea0-067c682d9a7f · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:07.529058Z

Source-reported events for the cited work

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

Observation 490633b7-c379-459f-a844-e60787546fad · outbound

This paper cites Abumohsen, A.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Abumohsen, A

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:07.763696Z

Source-reported events for the cited work

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

Observation 1cdd8626-5c57-437c-ae52-83a281c82d95 · outbound

This paper cites Salim and A.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Salim and A

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:07.635428Z

Source-reported events for the cited work

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

Observation 7f1adeed-80ff-4138-957f-c0582ac8a4d7 · outbound

This paper cites Joseph, S.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Joseph, S

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:07.178742Z

Source-reported events for the cited work

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

Observation 7f2aa726-bad4-4632-b525-e9e2db8aed56 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:07.391875Z

Source-reported events for the cited work

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

Observation f449c87a-42c7-4994-af5a-8a4fdb71661f · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:07.269234Z

Source-reported events for the cited work

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

Observation 23941648-ce62-43b1-9a97-bd151dad70d5 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:06.840258Z

Source-reported events for the cited work

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

Observation 4e2895d7-88e2-45e3-b461-c8599c038c9d · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:07.060854Z

Source-reported events for the cited work

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

Observation 1ee9b137-7a33-43da-a24e-23f71d0ae0cf · outbound

This paper cites Kantz and T.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Kantz and T

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:06.950117Z

Source-reported events for the cited work

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

Observation 4a0b073a-f40b-4077-bfb6-ac3084c79428 · outbound

This paper cites Samitas, E.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Samitas, E

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:06.423654Z

Source-reported events for the cited work

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

Observation 096ce718-ba5e-40b3-9701-d875773e02f3 · outbound

This paper cites Venkatesan and B.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Venkatesan and B

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:06.698228Z

Source-reported events for the cited work

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

Observation a0e30097-44d8-4d8e-86ee-32cf2d108989 · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:06.561277Z

Source-reported events for the cited work

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

Observation 771d6a9d-46ac-4836-922d-2293b944419a · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:05.887646Z

Source-reported events for the cited work

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

Observation d3033fce-8424-467d-b93b-76fc5bd2072e · outbound

This paper cites an unresolved cited work.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:41:06.262541Z

Source-reported events for the cited work

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

Observation c647ae8c-36a0-406b-be1f-893e40510b1c · outbound

This paper cites Ott, Chaos in Dynamical Systems (Cambridge Uni- versity Press, 2002).

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Ott, Chaos in Dynamical Systems (Cambridge Uni- versity Press, 2002)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:06.058714Z

Source-reported events for the cited work

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

Observation 35529361-aba8-46b1-8818-7ad407398382 · outbound

This paper cites Panel (c) shows an initial decrease in β followed by a plateau, highlighting the sensitivity of detection to variations in∆.

Approach to predicting extreme events in time series of chaotic dynamical systems using machine learning techniques Panel (c) shows an initial decrease in β followed by a plateau, highlighting the sensitivity of detection to variations in∆

Reference 180

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:41:09.217515Z

Source-reported events for the cited work

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

Pith citing papers

Observation 32b57336-9adc-4d2c-bff1-3c6a4bad9d49 · inbound