{"as_of":"2026-08-07T06:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5b1658d3dc8f35deffd25b0d0a4ff2118d4ddf632745a49fe8b73ebd81870551","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:01:27.272133Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-23T18:43:19.063599Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":"2310.15978","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph deep learning for time series forecasting","venue":null,"work_id":"95e31e89-f22b-4c6c-a077-522618c7802f","year":2023},"citing_paper":{"arxiv_id":"2410.13469","last_updated":"2026-04-07T16:53:29Z","snapshot_observed_at":"2026-07-06T19:35:13.136280Z","submitted_at":"2024-10-17T11:56:33Z","title":"Interpreting Temporal Graph Neural Networks with Koopman Theory","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-23T18:40:44.141686Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2410.13469"},"observation_digest":"sha256:dbac85ac3cb114ecbb0afc76e66cd3c93c25c113cfc281de5cb5fe47008e5b8f","observation_id":"f804ae03-3af1-4d85-85eb-b61da52a2990","resolution":{"observed_at":"2026-05-23T18:43:19.065589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-08-06T18:01:27.272133Z","title":"Graph deep learning for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09445","last_updated":"2025-08-02T05:17:13Z","snapshot_observed_at":"2026-08-07T01:31:23.305682Z","submitted_at":"2025-07-13T01:45:27Z","title":"Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T18:01:27.272133Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2507.09445"},"observation_digest":"sha256:6a43288b2f93a9d8409d56daffe477ea2d88381c6515bf92d3e50f6c19aab506","observation_id":"4de3d25f-dfa5-4d28-b0e3-013853517c0b","resolution":{"observed_at":"2026-08-06T18:01:27.272133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-08-06T17:09:15.166931Z","title":"”Graph deep learning for time series forecasting.” arXiv preprint arXiv:2310.15978 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-06T17:39:49.191194Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.166931Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:de80dcdb2ccc770911e1d4f984344166701d927b0d7d1a414a927248f0aa6858","observation_id":"a6d291d5-3456-49c8-a1ee-b4828d3f59d4","resolution":{"observed_at":"2026-08-06T17:09:15.166931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":"2310.15978","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph deep learning for time series forecasting","venue":null,"work_id":"95e31e89-f22b-4c6c-a077-522618c7802f","year":2023},"citing_paper":{"arxiv_id":"2507.13305","last_updated":"2026-05-05T17:59:26Z","snapshot_observed_at":"2026-07-06T21:58:51.796148Z","submitted_at":"2025-07-17T17:22:41Z","title":"Boosting Team Modeling through Tempo-Relational Representation Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-19T03:43:01.418431Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2507.13305"},"observation_digest":"sha256:901bc2b116b9a12227b4698ff64cc3735c928b770fd34bbb2e97cf835d3786c4","observation_id":"a0561a60-4e80-425b-8e13-8fe4944493ca","resolution":{"observed_at":"2026-05-19T03:47:02.475429Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":"2310.15978","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph deep learning for time series forecasting","venue":null,"work_id":"95e31e89-f22b-4c6c-a077-522618c7802f","year":2023},"citing_paper":{"arxiv_id":"2605.19172","last_updated":"2026-05-18T22:55:19Z","snapshot_observed_at":"2026-08-02T23:46:35.325374Z","submitted_at":"2026-05-18T22:55:19Z","title":"Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-20T11:43:29.621781Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2605.19172"},"observation_digest":"sha256:ce5678a21102b3c244a913fb21ce8c927e9332fdf146f57b8e8734ed6c515306","observation_id":"5de0e5c8-fc62-4f47-9cf1-2a5b6359a3bb","resolution":{"observed_at":"2026-05-20T11:48:15.235521Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.15978/citation-record","integrity":"/paper/2310.15978/integrity","json":"/paper/2310.15978/citation-record.json","paper":"/paper/2310.15978"},"outbound":[],"paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.15978."}