Paper Citation Record · LEDGER

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2505.16319.

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

pith.paper-citation-record.v1
2505.16319 v5

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

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measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T05:47:46.100657Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f67586b8-3993-453a-bd5a-eea480486125 · outbound

This paper cites Walmart recruit- ing - store sales forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Walmart recruit- ing - store sales forecasting

Reference 1

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Observation 82a7bc1e-8d93-44a5-bee4-b34a80ac548f · outbound

This paper cites Censored demand estimation in retail.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Censored demand estimation in retail

Reference 2

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Observation c67ccfde-d107-485f-b7e4-6717cda95ab9 · outbound

This paper cites Fore- casting with temporal hierarchies.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Fore- casting with temporal hierarchies

Reference 3

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Observation 9e8696fc-57ca-4e10-80c8-111036a70085 · outbound

This paper cites Inventory management with partially observed nonstationary demand.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Inventory management with partially observed nonstationary demand

Reference 4

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Observation e160cff7-dbff-4975-af82-b0078c019fb1 · outbound

This paper cites Kolmogorov–smirnov test: Overview.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Kolmogorov–smirnov test: Overview

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.668200Z

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Observation 909c279f-f046-4657-92d8-e0f2b67a7aa6 · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Recurrent neural networks for multivariate time series with missing values

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.598438Z

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Observation 3755ae2f-0343-4b09-8267-4f9a796742c5 · outbound

This paper cites Power-law distributions in empirical data.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Power-law distributions in empirical data

Reference 7

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Observation 1b5b24ea-80ac-48fe-980b-1ab270a73d0c · outbound

This paper cites SAITS: Self-Attention-based Imputation for Time Series.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail SAITS: Self-Attention-based Imputation for Time Series

Reference 8

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Observation db18fd84-4b5f-4e65-8707-141a327ae41c · outbound

This paper cites Dua and C.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Dua and C

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.450972Z

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Observation 7753143f-82e6-420a-bc85-547ecdfe99a1 · outbound

This paper cites Corpo- ración favorita grocery sales forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Corpo- ración favorita grocery sales forecasting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.373312Z

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 0424455f-f2e1-41c5-bf37-71c42134b6d3 · outbound

This paper cites Gp-vae: Deep probabilistic time series imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Gp-vae: Deep probabilistic time series imputation

Reference 11

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Observation e48e4fbd-97dc-4acd-b726-bc515386446d · outbound

This paper cites Offline dynamic inventory and pricing strategy: Addressing censored and dependent demand.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Offline dynamic inventory and pricing strategy: Addressing censored and dependent demand

Reference 12

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Observation 75dc03a1-1103-49a8-9d1b-90c95a3b1ba9 · outbound

This paper cites M5 forecasting - accuracy.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail M5 forecasting - accuracy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.234877Z

Source-reported events for the cited work

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Observation 52559b79-2f4a-4f92-a5d9-3fa49ec83b0a · outbound

This paper cites Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

Reference 14

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Observation bceac3c3-f645-4def-b718-ab202b72853a · outbound

This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Temporal fusion transformers for interpretable multi-horizon time series forecasting

Reference 15

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Source-reported events for the cited work

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Observation e96d9e46-2684-4eac-a292-177b7b53c396 · outbound

This paper cites Missing data assumptions.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Missing data assumptions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:24.053543Z

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 8ce4997b-894d-4365-ac6d-5fb5d21a84f0 · outbound

This paper cites Statistical analysis with missing data.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Statistical analysis with missing data

Reference 17

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Observation 1be1923d-0398-434a-be0c-74ee6eefa8fc · outbound

This paper cites Pristi: A conditional diffusion framework for spatiotemporal imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Pristi: A conditional diffusion framework for spatiotemporal imputation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.958737Z

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 34fd1b6f-c36c-4c8f-872f-15c8b4b4be0e · outbound

This paper cites Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.867325Z

Source-reported events for the cited work

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Observation cd3e7a6b-f3f8-4cf7-9a09-edc56b12a5ab · outbound

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

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 20

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Observation 34b63373-02ce-4599-9211-7af7df5f4322 · outbound

This paper cites Multivariate time series imputation with generative adversarial networks.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Multivariate time series imputation with generative adversarial networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.786794Z

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 8c81bc84-aa4e-465f-9885-3d5e9b594a82 · outbound

This paper cites M5 accuracy competi- tion: Results, findings, and conclusions.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail M5 accuracy competi- tion: Results, findings, and conclusions

Reference 22

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Observation 5174cfef-9660-4a78-a0d4-04bb38d3aba3 · outbound

This paper cites Demand estimation from censored observations with inventory record inaccuracy.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Demand estimation from censored observations with inventory record inaccuracy

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.623178Z

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 7f67248d-6eb3-4fd4-9be8-5842dd11f7cf · outbound

This paper cites Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.530259Z

Source-reported events for the cited work

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Observation 5f575148-6673-49c8-a656-a1e6bb07a3b8 · outbound

This paper cites Continuous inventory control with stochastic and non- stationary markovian demand.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Continuous inventory control with stochastic and non- stationary markovian demand

Reference 25

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

Observation 94b626fc-ebdc-42cc-bc95-419feeaf23ad · outbound

This paper cites Imputeformer: Low rankness- induced transformers for generalizable spatiotemporal imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Imputeformer: Low rankness- induced transformers for generalizable spatiotemporal imputation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.355469Z

Source-reported events for the cited work

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Observation 3c381284-8a2f-4220-89b7-6f11411c06ba · outbound

This paper cites Brazilian e-commerce public dataset by olist.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Brazilian e-commerce public dataset by olist

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.242480Z

Source-reported events for the cited work

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Observation c9a0ce1c-61ab-42c5-a2bd-ca11308d0d69 · outbound

This paper cites Pedregal and Juan R.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Pedregal and Juan R

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.154122Z

Source-reported events for the cited work

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Observation 293acfa1-740e-4a38-a79d-218c1ffb813f · outbound

This paper cites A cross-temporal hierarchical frame- work and deep learning for supply chain forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail A cross-temporal hierarchical frame- work and deep learning for supply chain forecasting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:23.054671Z

Source-reported events for the cited work

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Observation 713fc8a9-c271-4602-98e0-d5cacd8f914c · outbound

This paper cites Coherent probabilistic forecasting of temporal hierarchies.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Coherent probabilistic forecasting of temporal hierarchies

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.987478Z

Source-reported events for the cited work

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Observation 9fb0f61c-aee2-4143-80c8-1b7de59085f4 · outbound

This paper cites Inference and missing data.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Inference and missing data

Reference 31

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Observation 5cb2fb62-da3a-465a-b15e-b4d250e2b7b0 · outbound

This paper cites Arima models.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Arima models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.906317Z

Source-reported events for the cited work

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Observation a9c85797-97a4-42cb-bf5b-8d9048af3a78 · outbound

This paper cites Predicting demand for new products in fashion retailing using censored data.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Predicting demand for new products in fashion retailing using censored data

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.807289Z

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 8945ac64-2870-4a34-82a6-b12525364cc5 · outbound

This paper cites Csdi: Conditional score-based diffusion models for probabilistic time series imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Csdi: Conditional score-based diffusion models for probabilistic time series imputation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.688964Z

Source-reported events for the cited work

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Observation c1daffa9-e207-4e23-9922-dcc04cc48756 · outbound

This paper cites A behavioral remedy for the censorship bias.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail A behavioral remedy for the censorship bias

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.583874Z

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 10d4b23d-a4a2-4e02-897b-a69b29faf07b · outbound

This paper cites Attention is all you need.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Attention is all you need

Reference 36

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Source-reported events for the cited work

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Observation 1a3fd207-3389-49f5-ab70-3638d72d156a · outbound

This paper cites Gaussian processes for regression.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Gaussian processes for regression

Reference 37

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Source-reported events for the cited work

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Observation 8a0fd701-d0ee-44c7-8223-6fdefa5bb362 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 38

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Source-reported events for the cited work

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Observation fe2e513c-1825-4860-9315-034b066bf6a4 · outbound

This paper cites Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:21.476782Z

Source-reported events for the cited work

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Observation 8c94d7cc-e467-4ded-abf6-ccc05dd5f3b0 · outbound

This paper cites Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023

Reference 40

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Source-reported events for the cited work

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Observation 1cec1059-a862-481e-9eda-f63d0e90f152 · outbound

This paper cites Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:21.654619Z

Source-reported events for the cited work

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Observation 3496744a-727f-41ab-a6c2-c914aeea91e6 · outbound

This paper cites sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:21.772505Z

Source-reported events for the cited work

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Observation f3dc8f4d-cd3f-43cf-9122-7bf54f3377e6 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:21.862129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42a52438-ba6f-4021-ae88-a42c062417b2 · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:06:22.375344Z

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.

Pith citing papers

Observation c8dfa7af-446a-4cbf-9462-7985bc7ade28 · inbound

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination cites this paper.

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

Reference 11

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Source-reported events for the cited work

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