{"as_of":"2026-08-11T18:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0037d936438861b2bcaa73099b978ad9dc26cecb67af62930dfc5c9923f9ced8","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T04:40:51.660790Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.27863/citation-record","integrity":"/paper/2606.27863/integrity","json":"/paper/2606.27863/citation-record.json","paper":"/paper/2606.27863"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"M5 accuracy competition: Results, findings, and conclusions.International Journal of Forecasting, 38(4):1346–1364, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:a889bf8be0ddefc75465c06f4dac3af3b577031e580ba4765d17e191906b81a2","observation_id":"2089c2d5-1d35-4b47-b4f9-79a15c7bb0c8","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Corporación favorita grocery sales forecasting","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:1363710f426db744a7ef2c3011ea0f97152ebedd4ba5a48d259e7b92b8c8c75a","observation_id":"e4629d3c-1521-45b5-bceb-562aafcfe989","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Hyndman and George Athanasopoulos.Forecasting: Principles and Practice","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:c973efd08481fe0d2e9d60318a2893f5b46b62a2faa6c61681a12971861695d9","observation_id":"4b0b3b5c-37d8-4030-978f-82ab4b0a3d8a","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks.International Journal of Forecasting, 36(3):1181–1191, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:b4be858f2577b178f46303eb23c12a00f100afa4d7e066b55ffc26e046557ed4","observation_id":"b6d4c7fa-9607-424b-8e63-bba4d804e774","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Arık, Nicolas Loeff, and Tomas Pfister","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:083158276c945d264b3d6e9a5bc2c803435e8a9d5cd9380f1d63df69756edfc9","observation_id":"906dfa0c-ea93-4a8b-baea-0df7300fb863","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04615","last_updated":"2023-12-07T18:51:41Z","snapshot_observed_at":"2026-07-06T16:58:32.099526Z","submitted_at":"2023-12-07T18:51:41Z","title":"Relational Deep Learning: Graph Representation Learning on Relational Databases","version":1},"cited_work":{"arxiv_id":"2312.04615","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.04615","snapshot_observed_at":"2026-07-04T16:09:57.498942Z","title":"Relational deep learning: Graph representation learning on relational databases","venue":null,"work_id":"96c4eef5-2512-4101-a1b3-2b7040b0ae42","year":2023},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"cited_paper":"/paper/2312.04615","citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:c6e557744c96cff398de61d357b59008358ec2506bdb7960822996acb62ff5eb","observation_id":"8d778328-21e7-46c1-892b-245517396b65","resolution":{"observed_at":"2026-06-29T19:43:55.195708Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"RelBench: A benchmark for deep learning on relational databases","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:86438a0c98be083256ae606b7a68f6a54956867b75986078f21af30def3e3d87","observation_id":"a41be8bc-6206-423a-a93c-7c60bd60f215","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:2bb0ac5953bd109c1a1a036b2e6cca7f82f73e9248ab5577842dba9ed34746f5","observation_id":"c3b77348-1fc3-4a0c-8b98-d5c8a9280ff4","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Olivares, Boris N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:ff8f1631414f8902cbfb2b952b4f809526de795061fea6124ed0d0a057d1ca00","observation_id":"16a2b5b9-ef2b-4503-b0bd-534b2294f8f1","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Autoformer: Decomposition transformers with auto- correlation for long-term series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:a4990eed412cb32029f65bf1d6d26da9e3fa9fa558d19a3c03faecac64be04ec","observation_id":"a4e7a728-4618-44c0-bcf4-126889324a5d","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"The M4 competition: 100,000 time series and 61 forecasting methods.International Journal of Forecasting, 36(1):54–74, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:975eccdb26d5cc0108b081f818c9288e9482e3caff4ddc1910dfd20f597a74cc","observation_id":"bc34963f-1fbb-4625-8ead-c8306e0570a6","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Deep factors for forecasting","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:2f774f66976b2d92030d736fdc19f1e4cc0e34c78d37d99e0f3c51fe78c3d643","observation_id":"29e3baac-7a1f-42b7-aba0-ab21012add8a","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:a5e11c68fe4270dac28f25b92304a5ddf760f62eb40ac163c6723da7887196ed","observation_id":"72955c02-8cd1-4409-b240-71ec2ee53ef5","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:2d103d4504a78516bebc84e9e56a4a9d84424a15d6ffbaef48ebe6cf87c75fc2","observation_id":"27c62dd9-8adf-4cd4-bb54-ec74100cc093","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:e6ed896ac39bc297f0f98eb5eb6d3b0af110669c7e8a75531acf13d954397fcb","observation_id":"f9d8de19-6041-4dd2-95bf-f78e02a27892","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:c0369920cb445545f365901bfbcc9bd20b92357f2ea4eb5a49d840b1cd25375f","observation_id":"bf64ebe1-809d-403a-a5e7-73de35536938","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Graph wavenet for deep spatial-temporal graph modeling","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:2a8078bb57390c12304e45bac981197e4c2951d67f9510ccdfab378d11660c89","observation_id":"e9a375d8-eb51-4832-a5b7-01a1110e7246","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:783e3a64fa66877cab9986070b05cb973489ab260ad1a8b3e9012c929af5972e","observation_id":"af49df53-b7f0-41cf-a0db-8e032c70833f","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"Adaptive graph convolutional recurrent network for traffic forecasting","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:70131445ba8a57b7d3fc283c7bf0046b73767e83479d883defbaa9dd5f91006e","observation_id":"222dda5e-988a-4c1f-a861-e1c32c19a505","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:40:51.660790Z","title":"M5 Forecasting – Accuracy: Estimate the unit sales of walmart retail goods","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T04:40:51.660790Z"},"links":{"citing_paper":"/paper/2606.27863"},"observation_digest":"sha256:88406f7f1de861fffdb6b5ea56633f44b83c34eda489f2600da93b3d2686060d","observation_id":"c10c65b8-709e-4ea3-8eff-d397f496ee3a","resolution":{"observed_at":"2026-06-29T04:40:51.660790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.27863","last_updated":"2026-06-26T09:05:15Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T03:06:28.090101Z","submitted_at":"2026-06-26T09:05:15Z","title":"GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":20},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2606.27863."}