Paper Citation Record · LEDGER

Forecast-Then-Optimize Deep Learning Methods

As of 7 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2506.13036.

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

pith.paper-citation-record.v1
2506.13036 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:42:52.516626Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

9 of 9 outbound references displayed

verified exact0
verified fuzzy3
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 52e6c7a2-bbc4-4d4f-952a-1628288ad35d · outbound

This paper cites Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms.

Forecast-Then-Optimize Deep Learning Methods Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:42:52.598774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 409a359f-0761-40d9-b876-e07542947410 · outbound

This paper cites Adv Neural Inf Process Syst Smyl S (2020) A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting.

Forecast-Then-Optimize Deep Learning Methods Adv Neural Inf Process Syst Smyl S (2020) A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:52.635033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d351f0d3-2160-4e7f-b292-ad35c5a744c2 · outbound

This paper cites Xu Z, Zeng A, Xu Q (2023) FITS: Modeling Time Series with $10k$ Parameters.

Forecast-Then-Optimize Deep Learning Methods Xu Z, Zeng A, Xu Q (2023) FITS: Modeling Time Series with $10k$ Parameters

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:52.623886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f4f464a-9b1d-45a3-b495-00d8d68a972d · outbound

This paper cites Energy Reports 1:216–220.

Forecast-Then-Optimize Deep Learning Methods Energy Reports 1:216–220

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:52.612239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1305ccc2-b9c0-4261-aac9-4563522501c4 · outbound

This paper cites Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting.

Forecast-Then-Optimize Deep Learning Methods Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:52.512730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d332cbe-70e2-4d5f-bcaf-9ffe5174f12d · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Forecast-Then-Optimize Deep Learning Methods iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:52.494100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d7ee126-37ab-4583-b406-d441f25e534d · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Forecast-Then-Optimize Deep Learning Methods Linformer: Self-Attention with Linear Complexity

Reference 118

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:52.503631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f8692f-2e60-4fcc-bf7e-2df8fcd05f24 · outbound

This paper cites Reformer: The Efficient Transformer.

Forecast-Then-Optimize Deep Learning Methods Reformer: The Efficient Transformer

Reference 146

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:52.489431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a829294-7624-43eb-a810-be018f89cc28 · outbound

This paper cites TimeGPT-1.

Forecast-Then-Optimize Deep Learning Methods TimeGPT-1

Reference 468

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:52.484961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.