Paper Citation Record · LEDGER

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

As of 8 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 1 inbound Pith citation observation for arXiv:2509.07813.

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

pith.paper-citation-record.v1
2509.07813 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:44:24.218705Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T03:57:47.560737Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact3
verified fuzzy4
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89d517d9-8fa3-468f-ad75-54299cae6938 · outbound

This paper cites Z., & Koltun, V.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Z., & Koltun, V

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:44:25.751387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 635c4ba5-f69a-405b-88cb-b94b973f20a1 · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:44:25.606737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1dac98b-1f79-4783-bfb5-d8d1ec6e99d6 · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:44:25.460255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5fd6f846-27d2-4c71-ac24-dfb7433ef873 · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-04T21:44:25.300283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e38bba1-e0c7-4a8d-a660-f4eb4c94d346 · outbound

This paper cites J., & Athanasopoulos, G.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models J., & Athanasopoulos, G

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:44:25.158811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cecda39a-3a4a-431b-9e16-1099fdf62409 · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 6

Resolution: verified exact; doi, observed 2026-08-04T21:44:24.776719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d7cec820-c4ea-4f28-be7e-5f89c7ab565d · outbound

This paper cites A., G¨ artner, T., & Meyer, C.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models A., G¨ artner, T., & Meyer, C

Reference 7

Resolution: verified exact; doi, observed 2026-08-04T21:44:24.602096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7fee0d48-a201-4bab-a442-5f92c0448dbf · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 8

Resolution: verified exact; doi, observed 2026-08-04T21:44:24.415864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 41ad235a-2116-490c-ad10-ae5524dde9b5 · outbound

This paper cites J., & Letham, B.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models J., & Letham, B

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:44:25.045494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e1c23c7-6990-4393-9376-4834c24baa04 · outbound

This paper cites D., Greenhill, B.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models D., Greenhill, B

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:44:24.910949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eff82fa0-d296-4f6b-85e9-5f850bc151ff · outbound

This paper cites an unresolved cited work.

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T21:44:24.218705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0b481f20-8ff7-4a78-94ce-6e9e8574a489 · inbound

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions cites this paper.

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T03:57:47.560737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.