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Paper Citation Record · LEDGER

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2409.14327.

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

pith.paper-citation-record.v1
2409.14327 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:57:30.323360Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T22:44:13.663115Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d8e252cc-f82e-41ef-ad3c-97501098b2f5 · inbound

Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer cites this paper.

Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T15:57:30.323360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:57:30.323360Z digest=sha256:a6ee61189ae2076afeaa779489d8df225b4f94b7e577e51958e9b4d84cd79722

Observation 618b8673-81ec-4fd5-b02c-e0f5cde8e70e · inbound

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models cites this paper.

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T14:55:04.128683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:55:04.128683Z digest=sha256:9b47babae57f2bfff662b199c214f66cd890ff9359f9b4296fd933f462d71596

Observation 8d48c41b-288e-4e52-8c83-0f52599a69ed · inbound

Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data cites this paper.

Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T00:42:49.720439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:42:49.720439Z digest=sha256:2dc4b702eb764d050044151bf1990ea4ed1d43556f347cf054bb1f00f24d8807

Observation 748ef496-dc14-4404-abb8-ae18afdf8bd0 · inbound

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction cites this paper.

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-08T21:44:20.301743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T21:44:20.301743Z digest=sha256:4a7ec1511be4ad7313ba2330710bcbccbf770591842ef37903d6bce62dba9581

Observation 94fbea64-6d2a-41ed-872c-6eb9ad1bf0e2 · inbound

A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning cites this paper.

A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-07T22:44:13.671603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T22:44:13.515295Z digest=sha256:690d9fa053e751e324990b3fa0f03a0d19c207c4153b0969999c2775892fd00a