Paper Citation Record · LEDGER

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings

As of 3 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2606.27672.

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

pith.paper-citation-record.v1
2606.27672 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:54:39.686908Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+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

11 of 11 outbound references displayed

verified exact4
verified fuzzy0
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7357365b-54f2-4ac1-aa76-791a7a8979f7 · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings MOMENT: A Family of Open Time-series Foundation Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.903050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e6c79bce-0162-4aa6-abf1-e151deba8550 · outbound

This paper cites Chronos-2: From Univariate to Universal Forecasting.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Chronos-2: From Univariate to Universal Forecasting

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-06-29T19:13:52.908869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2482b7c2-e233-46f6-8b1d-f1a953501597 · outbound

This paper cites TimeGPT-1.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings TimeGPT-1

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.912021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6dbc6fd4-0985-4743-96bb-42bd6bb0aaeb · outbound

This paper cites Benchmarking a time-series foundation model (timegpt) for real-world forecasting applications,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Benchmarking a time-series foundation model (timegpt) for real-world forecasting applications,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c80515f-84ae-49ce-92bf-085704b5a6e2 · outbound

This paper cites Mira: Medical time series foundation model for real-world health data,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Mira: Medical time series foundation model for real-world health data,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4496c71-1f99-42f8-9fb7-ef336fa46c74 · outbound

This paper cites How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.905987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 14849b46-a6ba-4e8b-9c44-b0a10543f4c3 · outbound

This paper cites From detection to forecasting: utilizing time-series foundation models to anticipate defects in metal additive manufacturing,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings From detection to forecasting: utilizing time-series foundation models to anticipate defects in metal additive manufacturing,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3484ffe0-5c69-4f53-9643-20d5407f4205 · outbound

This paper cites Ultra low power mox sensor reading for natural gas wireless monitoring,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Ultra low power mox sensor reading for natural gas wireless monitoring,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a41c2ee7-ad49-447d-9af7-1a1db17450cf · outbound

This paper cites Mox-nw electronic nose for detection of food microbial contamination,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Mox-nw electronic nose for detection of food microbial contamination,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41be8ba9-2885-4af5-ab48-814dd86647d6 · outbound

This paper cites Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 557f8209-40aa-49ed-8ed0-1b8292adeb2d · outbound

This paper cites Mdfe-net: A meta-learning driven dual-branch feature extraction network for e-nose sensor drift adaptation,.

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings Mdfe-net: A meta-learning driven dual-branch feature extraction network for e-nose sensor drift adaptation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T04:54:39.686908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.