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Paper Citation Record · LEDGER

TimeNet: Pre-trained deep recurrent neural network for time series classification

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1706.08838.

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

pith.paper-citation-record.v1
1706.08838 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:29:22.688383Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:34:38.662890Z

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 8be5f3ae-0459-4037-b827-ea0a7719b098 · inbound

Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.

Modular Foundation Models for Time-Series Perception in Digital Twins TimeNet: Pre-trained deep recurrent neural network for time series classification

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-12T01:22:51.284207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:e8c05ed09da5540093e64bd54752e16b0d6aee5dff43c45c24733e93b5079b1e

Observation 59f43e88-8643-4b86-a55b-148795b1f6e3 · inbound

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? cites this paper.

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? TimeNet: Pre-trained deep recurrent neural network for time series classification

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:34:38.668271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T14:34:38.443537Z digest=sha256:4097b81813e224dae6f1c6191742ecc87150e5597ee8a97c5fedfef4983398c6

Observation 3cab6307-c622-4d0d-b169-f1458aa4be9e · inbound

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? cites this paper.

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? TimeNet: Pre-trained deep recurrent neural network for time series classification

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T04:29:22.688383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:29:22.688383Z digest=sha256:780aa7aa014b8e98549edc6909a247cefbea2e1e876b7ccf175561ba9edba07c