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Paper Citation Record · LEDGER

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach

As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2508.20795.

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

pith.paper-citation-record.v1
2508.20795 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:54:19.076626Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

13 of 13 outbound references displayed

  • verified exact1
  • verified fuzzy11
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87cf6f43-a27b-494d-ab3c-03f07017a8ca · outbound

This paper cites The combination of forecasts.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach The combination of forecasts

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.257964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.028785Z digest=sha256:56824254d6f9242de1b95ad8d71601c42419b6da9ea0ef0c27029791cebd57c4

Observation dea56a72-421e-4a37-8d1b-1165aebcf5d4 · outbound

This paper cites Kaggle forecasting competitions: An overlooked learning opportunity.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Kaggle forecasting competitions: An overlooked learning opportunity

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.246179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.032857Z digest=sha256:3a9885403facde720b2cff89a23d2ec5cb2b7952534167a1e86d6b6f40a126da

Observation b55b6861-a27d-4ba9-97fc-50bbda90861e · outbound

This paper cites Combining forecasts: A review and annotated bibliography.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Combining forecasts: A review and annotated bibliography

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.234268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.036854Z digest=sha256:ca017b4666fc76aa8e4a94403207478c7b12193c8f8b848e8a5c0196ff81d3f6

Observation 59b8da36-ee5f-4c24-b060-4bc34246f9eb · outbound

This paper cites Principled reward shaping for reinforcement learning via lyapunov stability theory.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Principled reward shaping for reinforcement learning via lyapunov stability theory

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.222473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.040721Z digest=sha256:85f3ab94ebca718e010c0bc3c7afa83b7fef970c78faf36f89c1e0aaac8f7761

Observation ad958a8e-1111-46cb-aae7-a44961976c7c · outbound

This paper cites Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.210406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.045708Z digest=sha256:d94c77378022b883eb05dca2a3455eab9111901f50c625ce3b6317ff7a481260

Observation e8032cfc-da53-4e38-acb4-2bea746faba5 · outbound

This paper cites Reinforcement learning based dynamic model selection for short-term load forecasting.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Reinforcement learning based dynamic model selection for short-term load forecasting

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.198349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.049284Z digest=sha256:f6d906ec763ea31d68c7ea93875cab703a5c6e1985b2f5b4e0dff104b7448194

Observation f0c3455b-0d0b-4bee-8faa-f3d7bee5f65e · outbound

This paper cites News Deja Vu: Connecting Past and Present with Semantic Search.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach News Deja Vu: Connecting Past and Present with Semantic Search

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:54:19.113427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.053196Z digest=sha256:1fc18763bb793dd3bdeb97926532effec3de2cbff5ad77b309d94e2488463e3d

Observation 030bfcc7-0513-4098-a8d7-aa8a3997c105 · outbound

This paper cites Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.185674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.057048Z digest=sha256:0f672904909111f60ba795ab307a4b7445de693488ed0fa246a33f74df5773b2

Observation 011274ec-9305-4426-9eb1-da9b3c82fba6 · outbound

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

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach The m4 competition: 100,000 time series and 61 forecasting methods

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.172077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.060484Z digest=sha256:d04554f34f5587ee76630874505fdd04c13c39bfffd23ca1b3bba128e6fa774f

Observation adce6f4b-8a86-4089-a3a1-dee81b6c243d · outbound

This paper cites M5 accuracy competition: Results, findings, and conclusions.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach M5 accuracy competition: Results, findings, and conclusions

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.159164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.064904Z digest=sha256:04fc51037016f481aab0c19d3b0ff10af542ac08398b31d63f0c133b364a645b

Observation f8745335-27f7-4ef9-a519-348d5e385a57 · outbound

This paper cites Machine learning dynamic switching approach to forecasting in the presence of structural breaks.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Machine learning dynamic switching approach to forecasting in the presence of structural breaks

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.146387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.068612Z digest=sha256:287bfe9edd5fdd343a310ac47d12b085648c027a6d71adcbc708f5bd150fd96e

Observation 3823672f-f30d-4189-a2b1-850a06fc1dff · outbound

This paper cites Reward is enough.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Reward is enough

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:54:19.133787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:54:19.072539Z digest=sha256:da1c042822d5296f2016d8eee0226d070502b3c1912d8ac9f8f1aa35caac1ac2

Observation 8cba03bf-b0f6-49cc-8b40-0667bbe36581 · outbound

This paper cites Learning to predict by the methods of temporal differences.

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach Learning to predict by the methods of temporal differences

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-05T14:54:19.076626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:54:19.076626Z digest=sha256:1b17fe65d10652ed47d9a0b9da75920d9ba64cfdad6fc8f1a667d117d2da61ba

Pith citing papers

No inbound Pith citation observations are available.