Paper Citation Record · LEDGER

Evaluating Generative Time-Series Models on Data with Point Masses

As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2608.09692.

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

pith.paper-citation-record.v1
2608.09692 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:39:57.059435Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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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

30 of 30 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f6e79e5-fe49-47a8-bbc1-c00ddf42a797 · outbound

This paper cites Flow matching with gaussian process priors for probabilistic time series forecasting,.

Evaluating Generative Time-Series Models on Data with Point Masses Flow matching with gaussian process priors for probabilistic time series forecasting,

Reference 1

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Observation cba1a935-15ae-4692-bddc-dbe7ac16b558 · outbound

This paper cites PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation.

Evaluating Generative Time-Series Models on Data with Point Masses PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation

Reference 2

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Observation e4ba16f4-c434-4e09-aa27-88ad0a9c04fc · outbound

This paper cites Timeflow: Towards stochastic-aware and efficient time series generation via flow matching modeling,.

Evaluating Generative Time-Series Models on Data with Point Masses Timeflow: Towards stochastic-aware and efficient time series generation via flow matching modeling,

Reference 3

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Observation 26ca3af6-ccca-49fc-ba57-ffdb4c1d912a · outbound

This paper cites SDFlow: Similarity-Driven Flow Matching for Time Series Generation.

Evaluating Generative Time-Series Models on Data with Point Masses SDFlow: Similarity-Driven Flow Matching for Time Series Generation

Reference 4

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Observation d46a19d0-89f1-4428-94d5-86a8080686d9 · outbound

This paper cites A non-isotropic time series diffusion model with moving average transitions,.

Evaluating Generative Time-Series Models on Data with Point Masses A non-isotropic time series diffusion model with moving average transitions,

Reference 5

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Observation 01d32bfe-f268-4060-8dec-0fe8ea2eae74 · outbound

This paper cites Non-stationary diffusion for probabilistic time series forecasting,.

Evaluating Generative Time-Series Models on Data with Point Masses Non-stationary diffusion for probabilistic time series forecasting,

Reference 6

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Observation d20b195c-f00d-4b4a-b4b6-0aceb7e807dd · outbound

This paper cites Time-series generative ad- versarial networks,.

Evaluating Generative Time-Series Models on Data with Point Masses Time-series generative ad- versarial networks,

Reference 7

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Observation 179be448-c63b-4302-89f5-41fd4c032884 · outbound

This paper cites PSA-GAN: Progressive self attention GANs for synthetic time series,.

Evaluating Generative Time-Series Models on Data with Point Masses PSA-GAN: Progressive self attention GANs for synthetic time series,

Reference 8

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Observation 5cf2d845-45a9-4e6e-901e-6fcc822d951c · outbound

This paper cites Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting,.

Evaluating Generative Time-Series Models on Data with Point Masses Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.487262Z

Source-reported events for the cited work

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Observation e50c9929-c683-4d5b-aa02-9752350a75da · outbound

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

Evaluating Generative Time-Series Models on Data with Point Masses Csdi: Conditional score- based diffusion models for probabilistic time series imputation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.475094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81574bc5-0da1-4a7b-8650-ea9d6c7a1b07 · outbound

This paper cites Diffusion-ts: Interpretable diffusion for general time series generation,.

Evaluating Generative Time-Series Models on Data with Point Masses Diffusion-ts: Interpretable diffusion for general time series generation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.463124Z

Source-reported events for the cited work

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Observation d0c55ae5-2cdc-4d03-b261-7a7d1a4ccaa1 · outbound

This paper cites Flow matching for generative modeling,.

Evaluating Generative Time-Series Models on Data with Point Masses Flow matching for generative modeling,

Reference 12

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Source-reported events for the cited work

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Observation cf48ae30-8b10-47b1-852b-df0ff9a4d29e · outbound

This paper cites Forecasting and stock control for intermittent demands,.

Evaluating Generative Time-Series Models on Data with Point Masses Forecasting and stock control for intermittent demands,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.437256Z

Source-reported events for the cited work

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Observation dde37177-6623-4359-a345-4ea9e54225a0 · outbound

This paper cites The accuracy of intermittent demand estimates,.

Evaluating Generative Time-Series Models on Data with Point Masses The accuracy of intermittent demand estimates,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.424295Z

Source-reported events for the cited work

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Observation 2b1397d4-9605-475f-8351-bb363c2eda26 · outbound

This paper cites Intermittent demand forecasts with neural networks,.

Evaluating Generative Time-Series Models on Data with Point Masses Intermittent demand forecasts with neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.407406Z

Source-reported events for the cited work

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Observation 1a26e972-6dbb-42f0-8c97-eb339962dea8 · outbound

This paper cites Forecast- ing intermittent and sparse time series: A unified probabilistic framework via deep renewal processes,.

Evaluating Generative Time-Series Models on Data with Point Masses Forecast- ing intermittent and sparse time series: A unified probabilistic framework via deep renewal processes,

Reference 16

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Source-reported events for the cited work

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Observation e061771b-4a61-4568-99d4-b781893b3d0e · outbound

This paper cites Another look at measures of forecast accuracy,.

Evaluating Generative Time-Series Models on Data with Point Masses Another look at measures of forecast accuracy,

Reference 17

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Source-reported events for the cited work

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Observation 55f80679-a55a-4199-8853-ae54c3383fa8 · outbound

This paper cites Precipitation as a chain-dependent process,.

Evaluating Generative Time-Series Models on Data with Point Masses Precipitation as a chain-dependent process,

Reference 18

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Source-reported events for the cited work

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Observation 087a5398-cc14-4866-aab2-7aab52a41ed8 · outbound

This paper cites Stochastic simulation of daily precipitation, temper- ature, and solar radiation,.

Evaluating Generative Time-Series Models on Data with Point Masses Stochastic simulation of daily precipitation, temper- ature, and solar radiation,

Reference 19

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Observation a3c283f5-f1bd-4abd-8bb6-112b8294c903 · outbound

This paper cites A model fitting analysis of daily rainfall data,.

Evaluating Generative Time-Series Models on Data with Point Masses A model fitting analysis of daily rainfall data,

Reference 20

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Source-reported events for the cited work

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Observation d90da96a-5b6a-4913-a948-9cff3be8054e · outbound

This paper cites Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain,.

Evaluating Generative Time-Series Models on Data with Point Masses Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.337889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b5961828-f508-4392-a2ea-59ade544fead · outbound

This paper cites Estimation of relationships for limited dependent variables,.

Evaluating Generative Time-Series Models on Data with Point Masses Estimation of relationships for limited dependent variables,

Reference 22

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Source-reported events for the cited work

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Observation a741aecf-7840-43fd-b272-3ccfa5e9926b · outbound

This paper cites Tobit models: A survey,.

Evaluating Generative Time-Series Models on Data with Point Masses Tobit models: A survey,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.314037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 47cb85c2-278a-4840-8401-fff05bbb6a74 · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation,.

Evaluating Generative Time-Series Models on Data with Point Masses Strictly proper scoring rules, prediction, and estimation,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8d4d6a3b-b03c-4f78-ad63-51026becf4a6 · outbound

This paper cites Ts2vec: Towards universal representation of time series,.

Evaluating Generative Time-Series Models on Data with Point Masses Ts2vec: Towards universal representation of time series,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.284829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 11b17a18-34d0-40ab-b80c-4233aa926791 · outbound

This paper cites Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities,.

Evaluating Generative Time-Series Models on Data with Point Masses Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.272889Z

Source-reported events for the cited work

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Observation 68bc2139-9ce5-46f2-a921-792fc1701558 · outbound

This paper cites The schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields,.

Evaluating Generative Time-Series Models on Data with Point Masses The schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.260293Z

Source-reported events for the cited work

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Observation bc873d57-3cd1-48f2-a7dd-92491e7d5f16 · outbound

This paper cites Uncertainty quantifi- cation in complex simulation models using ensemble copula coupling,.

Evaluating Generative Time-Series Models on Data with Point Masses Uncertainty quantifi- cation in complex simulation models using ensemble copula coupling,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:39:57.248426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cf11dbbf-1987-4520-ae49-3d07b867b641 · outbound

This paper cites A similarity-based implementation of the schaake shuffle,.

Evaluating Generative Time-Series Models on Data with Point Masses A similarity-based implementation of the schaake shuffle,

Reference 29

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Source-reported events for the cited work

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Observation 8798a95b-5241-4789-9509-06eb97dc16de · outbound

This paper cites Monash Time Series Forecasting Archive.

Evaluating Generative Time-Series Models on Data with Point Masses Monash Time Series Forecasting Archive

Reference 30

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Source-reported events for the cited work

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Pith citing papers

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