Paper Citation Record · LEDGER

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling

As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2508.18922.

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

pith.paper-citation-record.v1
2508.18922 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:08:29.962630Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy12
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e22fd59-964a-40fb-a47a-a12b7a27730f · outbound

This paper cites Tactis: Transformer-attentional copulas for time series.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Tactis: Transformer-attentional copulas for time series

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:32.702535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4db4598f-8e68-4422-a82d-efc9f535644c · outbound

This paper cites Weight uncertainty in neural networks.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Weight uncertainty in neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:32.559260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2442247c-7766-41e7-aac1-597d8e0eaaa8 · outbound

This paper cites Optimization methods for large-scale machine learning.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Optimization methods for large-scale machine learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:27.619679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ccd4e1-bb5c-4ba2-b58f-485544bb657d · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:27.794898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 513a50c8-75e1-4cb2-822a-fa6404c63625 · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Gp-vae: Deep probabilistic time series imputation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:32.304421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a515994-5825-42d5-beb8-80c33decdce5 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:32.096179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed847540-a1c9-49d4-bc27-536138eb0ea0 · outbound

This paper cites Deep residual learning for image recognition.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Deep residual learning for image recognition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.200943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1dd5cde-5451-4fc2-9712-661982ca483c · outbound

This paper cites Long short-term memory.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Long short-term memory

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.295910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 662ee59b-a2d0-4bce-8ea1-e2d42e48453e · outbound

This paper cites Auto-encoding variational bayes.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Auto-encoding variational bayes

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.405127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dfa6ead-bcde-481b-acb3-a012b1be207f · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.518727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc60eb50-1043-4d23-a1b5-5325c1fa8a4e · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling A time series is worth 64 words: Long-term forecasting with transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.672048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da4f0440-30ba-49f3-8585-0a23fdcb5379 · outbound

This paper cites Estimating the mean and variance of the target probability distribution.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Estimating the mean and variance of the target probability distribution

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:28.820591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97daa45b-3e45-4c70-8ba1-35c75c7c86bd · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:31.792317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de1b9007-853a-4c8f-8a77-d664eacc370d · outbound

This paper cites Variational inference with normalizing flows.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Variational inference with normalizing flows

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:31.575312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6fa4cc36-02b9-4a93-a4da-f8b2115eea25 · outbound

This paper cites Learning structured output representation using deep conditional generative models.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Learning structured output representation using deep conditional generative models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:31.421927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b288fde-21ae-49c0-9f47-e970d488a424 · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Csdi: Conditional score-based diffusion models for probabilistic time series imputation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:31.138067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 809a18ee-659f-4bfd-b0a6-9898a4e155d2 · outbound

This paper cites Attention is all you need.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Attention is all you need

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:30.926076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8553af5-1c2a-4113-a7d1-d491b1b3e358 · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:30.736769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63306e45-2a2e-40dc-a8ae-6381517fe39c · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T16:08:29.710685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c2b7b64-ab9c-4c0a-9ede-b9617daba095 · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:30.518515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 042ee80c-8031-46a1-adb2-f62788d39f8e · outbound

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

HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:08:30.326436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.