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Paper Citation Record · LEDGER

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 2 inbound Pith citation observations for arXiv:2505.23017.

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

pith.paper-citation-record.v1
2505.23017 v3

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:02:35.657607Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:54:42.603821Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:54:44.165671Z

Reference resolution

20 of 20 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ee807f5-c991-4f4d-b1a6-7314ea426371 · outbound

This paper cites A com- prehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting A com- prehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5c114755-6da5-4621-98bf-30aa241bbda9 · outbound

This paper cites Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e2556cb2-de40-4c74-848c-3904ceb3fef0 · outbound

This paper cites Score: Story coherence and re- trieval enhancement for ai narratives.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Score: Story coherence and re- trieval enhancement for ai narratives

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9b2b95a3-45ac-4dd1-bf95-830eb2e2a9f4 · outbound

This paper cites Ginar: An end-to-end multivariate time se- ries forecasting model suitable for variable missing.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Ginar: An end-to-end multivariate time se- ries forecasting model suitable for variable missing

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T13:02:37.772014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T13:02:35.176757Z digest=sha256:57cbe1459787e5cbb1570c31ea13bed43a8b06ef2b52fa24b637a391a82c1841

Observation 7a53f4de-b7b7-40fc-88cd-a4056a624f73 · outbound

This paper cites Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:02:35.241122Z digest=sha256:89e9894fab63751536d92a9272338e4498aec419c07d47dfeed9650c2adfa93a

Observation a223b19b-df39-4adf-b081-da1b023cf28f · outbound

This paper cites IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning

Reference 15

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metadata mismatch
local_arxiv, observed 2026-08-07T13:02:36.172230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8b5cda00-f102-4da5-aab7-3af99a88aa00 · outbound

This paper cites fine-tuned.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting fine-tuned

Reference 16

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raw_fallback, observed 2026-08-07T13:02:37.512141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 574c3544-07c6-47e6-a7b8-22886d9a8ff5 · outbound

This paper cites However, these methods still require manual feature engineering and model design.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting However, these methods still require manual feature engineering and model design

Reference 17

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raw_fallback, observed 2026-08-07T13:02:37.209046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 28d0760f-4522-4b5b-8873-882fe42265d0 · outbound

This paper cites Electricity.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Electricity

Reference 18

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raw_fallback, observed 2026-08-07T13:02:36.986806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9fb74e01-232b-4aef-ba0d-15cde23047df · outbound

This paper cites an unresolved cited work.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Unresolved cited work

Reference 336

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raw_fallback, observed 2026-08-07T13:02:35.969671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation de6796d3-4597-443f-b09f-9882e7f7f17b · outbound

This paper cites Drop Last.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Drop Last

Reference 1976

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raw_fallback, observed 2026-08-07T13:02:36.870019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f73ee4b2-33ef-47b8-86ab-614e2c5e805c · outbound

This paper cites Prompt Categories Cluster for Weakly Supervised Semantic Segmentation.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Prompt Categories Cluster for Weakly Supervised Semantic Segmentation

Reference 1995

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 73c8c370-fecd-4684-a5c3-97cc4ce2b6f0 · outbound

This paper cites B., Gudelek, M.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting B., Gudelek, M

Reference 2010

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verified fuzzy
raw_fallback, observed 2026-08-07T13:02:37.979469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T13:02:34.878220Z digest=sha256:82d69262a12f8cc3380e3b6ec03ec728a0a1753c4ebfa08035dfacaeedc4bcf4

Observation 76cff73e-203c-404c-b9bb-c5bc289eb119 · outbound

This paper cites GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization

Reference 2016

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:02:34.282276Z digest=sha256:4901b95eb43625202a9721d528e3833f2ec6aea753fe15f29cf880aa67336e7b

Observation 48d04535-821d-4314-9082-9356dca238a1 · outbound

This paper cites MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

Reference 2017

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no resolver link, observed 2026-08-07T13:02:34.667512Z

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Unavailable: canonical work link unavailable.

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Observation c2a83de3-38e4-40c6-95d6-3d1137ce6db2 · outbound

This paper cites Multi-scale attention flow for probabilistic time series forecasting.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Multi-scale attention flow for probabilistic time series forecasting

Reference 2021

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verified fuzzy
raw_fallback, observed 2026-08-07T13:02:38.242369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1b929d2d-b094-4594-b215-a5ce0af36bb7 · outbound

This paper cites CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation

Reference 2022

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verified exact
local_arxiv, observed 2026-08-07T13:02:36.584843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5a8af782-673f-48d2-8410-bdd81e62995f · outbound

This paper cites Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery

Reference 2023

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local_arxiv, observed 2026-08-07T13:02:36.755226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T13:02:34.384345Z digest=sha256:35b44e8fbfc2ca74352a0ec5d268768393f42c6a5bd1037719e269566957566a

Observation f028a253-2a36-4953-adeb-9198ff8adef4 · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:02:34.458855Z digest=sha256:9cad1340388793eeed25561a13a6890ff79563da42ca606cf1243a999bd41780

Observation 69cc9911-1cde-4c96-874f-59a6546b6c19 · outbound

This paper cites Monash Time Series Forecasting Archive.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting Monash Time Series Forecasting Archive

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 83ac81f8-934a-47ef-8ff9-c743d6f7289e · inbound

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation cites this paper.

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

Reference 105

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local_arxiv, observed 2026-08-06T21:54:44.170587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation dbe6af08-6690-40df-bd43-f9878802ade4 · inbound

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting cites this paper.

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

Reference 19

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no resolver link, observed 2026-08-03T05:00:56.136190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:00:56.136190Z digest=sha256:204120810af41ef979e02cb26076dd2fc899421bf5da8fb5bfbc2a2c3d203cd7