{"as_of":"2026-08-08T13:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:37a03ca7561e6c655008b57928d99a8c5fe7bac0cba652267eb2bb114e39cba1","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T17:08:10.133832Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.17758/citation-record","integrity":"/paper/2607.17758/integrity","json":"/paper/2607.17758/citation-record.json","paper":"/paper/2607.17758"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T17:08:07.378407Z","title":"Ai for crisis decisions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.378407Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:94801275c5ac0b6898718bb1cb6bb82cba6c15856bd4f92839ebfd6e3dc22f83","observation_id":"5f27a336-ff84-4d37-a75b-e25da33f75c0","resolution":{"observed_at":"2026-08-01T17:08:07.378407Z","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":"2026-08-01T17:08:07.453904Z","title":"Comparison of three algorithms for real-time pedestrian state estimation-supporting a monitoring dashboard for large-scale events,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.453904Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:49611ec4f593a9cd360d3bed5b8b7cafd205485181ba01f78ba8fef8bebe883a","observation_id":"1e48f26c-2554-4b83-9488-97ef16241e34","resolution":{"observed_at":"2026-08-01T17:08:07.453904Z","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":"2026-08-01T17:08:07.545253Z","title":"Strictly proper scoring rules, prediction, and estimation,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.545253Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:aca8de634ded3dcb43f611602db539673075d96182c88fd778c43cb0b9bee915","observation_id":"d22b75f3-2751-49c7-848f-fa7800f72b26","resolution":{"observed_at":"2026-08-01T17:08:07.545253Z","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":"2026-08-01T17:08:07.666761Z","title":"Probabilistic forecasting,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.666761Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:568eadd27cb33420a5081e49185e26ba0a401313387d88b0fc1f7ff2b584b9d6","observation_id":"3571d993-296c-4cbc-9473-e97b22e2710a","resolution":{"observed_at":"2026-08-01T17:08:07.666761Z","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":"2026-08-01T17:08:07.750162Z","title":"Robust probabilistic time series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.750162Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:8928a864134a1891f6ee8608df206ca5277666db8c8ea8caa9ea80f3b39c3e82","observation_id":"97760551-6ba1-485e-ae32-8d7fe3e23599","resolution":{"observed_at":"2026-08-01T17:08:07.750162Z","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":"2026-08-01T17:08:07.840296Z","title":"Traffic flow prediction with big data: A deep learning approach,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.840296Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:fac802cd44b7021c58046bbd5bc87c86991f5788a356cbe5af248ea249e21560","observation_id":"21492c07-3826-442f-8e85-52f30a3b2a29","resolution":{"observed_at":"2026-08-01T17:08:07.840296Z","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":"2026-08-01T17:08:07.929678Z","title":"Dynamic spatial- temporal graph convolutional neural networks approach for active mode traffic prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:07.929678Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:6c18377815bb192a37e868e3835bf2dd3dadce2c2bdcbd5507c1bd4dfd681814","observation_id":"44728bd7-b77f-4cf7-8c06-878b25256573","resolution":{"observed_at":"2026-08-01T17:08:07.929678Z","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":"2026-08-01T17:08:08.019404Z","title":"A decoder-only foundation model for time-series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.019404Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:a43b0670e23f08dd50e5912d8ffa9a86555af8961a590bedeb8d8961278c0670","observation_id":"555f1117-d334-4bf5-baf6-c6b7b1c70bfa","resolution":{"observed_at":"2026-08-01T17:08:08.019404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.15821","last_updated":"2025-10-17T17:00:53Z","snapshot_observed_at":"2026-07-06T22:32:57.286856Z","submitted_at":"2025-10-17T17:00:53Z","title":"Chronos-2: From Univariate to Universal Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.15821","snapshot_observed_at":"2026-08-01T17:08:08.112645Z","title":"Chronos-2: From univariate to universal forecasting,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.112645Z"},"links":{"cited_paper":"/paper/2510.15821","citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:7fd756be7b1327920ea4f0e32527f497f6cfffa046c37d4a72d00dfae33a427f","observation_id":"fdbd6ad8-10e7-48b6-8de1-020448ca1c62","resolution":{"observed_at":"2026-08-01T17:08:08.112645Z","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":"2026-08-01T17:08:08.182104Z","title":"Frequency enhanced pre-training for cross-city few-shot traffic forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.182104Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:94595d864a248bdb4cf7ebae08841d3d6ef70312916b0ecf761c8850337d8cf6","observation_id":"1afd7fa8-72fe-4c79-bdff-b790acf1696f","resolution":{"observed_at":"2026-08-01T17:08:08.182104Z","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":"2026-08-01T17:08:08.260147Z","title":"Communicating disaster risk,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.260147Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:7f7b8830783fcc298221cad4a17c3fe222a502b52e043fa12387d9b8fd663ed9","observation_id":"50fac65b-d5ad-402f-9dad-98bd95bd4c85","resolution":{"observed_at":"2026-08-01T17:08:08.260147Z","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":"2026-08-01T17:08:08.334715Z","title":"Deepar: Probabilistic forecasting with autoregressive recurrent networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.334715Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:0bac2b5ae0a4b87641f8b0f920072d033134f6b86663221c17a1614e92895afd","observation_id":"7c50ad7a-2522-4983-baa4-d0e27ba84523","resolution":{"observed_at":"2026-08-01T17:08:08.334715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10437","last_updated":"2020-02-20T21:08:57Z","snapshot_observed_at":"2026-08-06T09:33:52.442294Z","submitted_at":"2019-05-24T20:28:57Z","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10437","snapshot_observed_at":"2026-08-01T17:08:08.399735Z","title":"N-beats: Neural basis expansion analysis for interpretable time series forecasting,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.399735Z"},"links":{"cited_paper":"/paper/1905.10437","citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:99a7a84cdf7ab3c9c8929a2cd2524a785f9903ab6a26b746abc1f5b4c180dc32","observation_id":"6a4e8b34-32a3-4799-a29f-e9905881c68e","resolution":{"observed_at":"2026-08-01T17:08:08.399735Z","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":"2026-08-01T17:08:08.470291Z","title":"Are transformers effective for time series forecasting?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.470291Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:5f81621c2501bc78cc9a96e34e949177fa90215dd69ac32c73a174fbf94a44ce","observation_id":"f8aeb011-1eb5-44f3-80f8-e6179520160b","resolution":{"observed_at":"2026-08-01T17:08:08.470291Z","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":"2026-08-01T17:08:08.543644Z","title":"Deep learning models for time series forecasting: A review,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.543644Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:90b41325ade5eb87e38584353f575f0039f898f2493d4cf64c9481a26715aa88","observation_id":"1ec1487b-f74c-4219-a22b-441ebff51855","resolution":{"observed_at":"2026-08-01T17:08:08.543644Z","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":"2026-08-01T17:08:08.633078Z","title":"Quantileformer: Probabilistic time series forecasting with a pattern-mixture decomposed vae transformer,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.633078Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:6f41d379ab6ce21732859a8126034bd98b13e9e64aa211f2f9274e20723253da","observation_id":"cb0fbfc8-2628-4366-8846-af63577458c6","resolution":{"observed_at":"2026-08-01T17:08:08.633078Z","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":"2026-08-01T17:08:08.726812Z","title":"Conformal prediction for time-series forecasting with change points,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.726812Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:57bd62a8a1ee2e3f4396f98265439488d1f40a7c8cdc29e0d4a01f4917505401","observation_id":"d6d402be-9e07-4156-87cd-fbcf0a6eccf4","resolution":{"observed_at":"2026-08-01T17:08:08.726812Z","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":"2026-08-01T17:08:08.796532Z","title":"Neural conformal control for time series forecasting,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.796532Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:5edf31c11b4cde64e51293f58c6372aae76009da4cafbc76f3e8a9e0ca00ee38","observation_id":"89370aba-aacb-4a07-9886-8d9776b3413c","resolution":{"observed_at":"2026-08-01T17:08:08.796532Z","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":"2026-08-01T17:08:08.870764Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.870764Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:a34d90f533b0427fb8163d4dd56e73caa335a71e6c58340311d48c671f64da1f","observation_id":"e3a4bf6f-315c-4448-b352-9026816e8d94","resolution":{"observed_at":"2026-08-01T17:08:08.870764Z","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":"2026-08-01T17:08:08.937705Z","title":"Large language models are zero-shot time series forecasters,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:08.937705Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:039b1b9a059ff261aa1685e83a04c2244147662123a4a6e28f4d67ae03311fa1","observation_id":"c7145a48-6b29-45d6-bacd-ceb78047f2a6","resolution":{"observed_at":"2026-08-01T17:08:08.937705Z","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":"2026-08-01T17:08:09.003510Z","title":"Experienced travel time prediction for congested freeways,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.003510Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:2c7c3885bec9e2aae530a3ed708f75ee0a324034fa5495217c706f3ac986b03a","observation_id":"cd6792af-c043-4a93-9685-6c041285345e","resolution":{"observed_at":"2026-08-01T17:08:09.003510Z","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":"2026-08-01T17:08:09.080157Z","title":"Public transit for special events: Ridership prediction and train scheduling,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.080157Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:f382645d9356c10ef62435ca52e68675f44fd09bacbbeb73c316de906a64019b","observation_id":"1620dd5c-4262-4c52-aa40-cba47c1dbd8e","resolution":{"observed_at":"2026-08-01T17:08:09.080157Z","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":"2026-08-01T17:08:09.160860Z","title":"Compre- hensive review of neural network-based prediction intervals and new advances,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.160860Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:5423d7d45a44a140fed9bee552c5756e474c59ddb3cbf6090513fb7bd261e3e0","observation_id":"c49b61e2-e22b-48ad-a15c-86abde58bd17","resolution":{"observed_at":"2026-08-01T17:08:09.160860Z","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":"2026-08-01T17:08:09.250036Z","title":"A decision-theoretic approach to interval estimation,","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.250036Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:a48d0547b03f5fe12ea045044a81d72a184fdc54007d30c6351b830cfd03c677","observation_id":"2b267315-7011-4f78-a92f-945d60525ec4","resolution":{"observed_at":"2026-08-01T17:08:09.250036Z","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":"2026-08-01T17:08:09.340097Z","title":"A gat-bilstma model for weather-aware prediction of traffic speed,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.340097Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:cc76391894a368eb26d530dd2d7ca207f4c7f23a0901b8a010a88969ef69162c","observation_id":"85457273-16e0-49d2-bcf9-279d0a3a5dc6","resolution":{"observed_at":"2026-08-01T17:08:09.340097Z","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":"2026-08-01T17:08:09.402070Z","title":"Metro ridership forecasting using inter-station-aware transformer networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.402070Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:abba65a9af5538bc027535d146877ccc536f703c8800826d0748648011e716dc","observation_id":"93eb8d79-3a81-4d0c-aa38-027fecdd08ec","resolution":{"observed_at":"2026-08-01T17:08:09.402070Z","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":"2026-08-01T17:08:09.490761Z","title":"Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.490761Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:591aeb7cfe8188225899ca407421cb6e1b8f53f6c54f2b8c65678d31af3fa167","observation_id":"47f7b09e-f693-490c-ac1b-955cf0e08935","resolution":{"observed_at":"2026-08-01T17:08:09.490761Z","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":"2026-08-01T17:08:09.570949Z","title":"Bi-level model predictive control for metro networks: Integration of timetables, passenger flows, and train speed profiles,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.570949Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:9baec610fbeb5b63206b6d97f7ef09f074fccb1fd2cf7844563a9f771d060ac4","observation_id":"3f004c72-bd3c-4712-ae3d-cbab622049c7","resolution":{"observed_at":"2026-08-01T17:08:09.570949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-07-31T22:45:35.561492Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-01T17:08:09.730562Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.730562Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:a19c23b0a8ed9ad102561b34623fea000e29ea35964cfce03ee6338e22a76681","observation_id":"fe6c08e1-5504-4301-b71e-802603efbe65","resolution":{"observed_at":"2026-08-01T17:08:09.730562Z","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":"2026-08-01T17:08:09.847217Z","title":"Deformabletst: Transformer for time series forecasting without over-reliance on patching,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:09.847217Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:538e3f61f0c3dc5f98c6797430785a2e746588458305b3e49b8433d408385a6c","observation_id":"933e93d0-04b1-4b33-950d-f11275daeedc","resolution":{"observed_at":"2026-08-01T17:08:09.847217Z","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":"2026-08-01T17:08:10.000876Z","title":"Learning pattern-specific experts for time series forecasting under patch-level distribution shift,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:10.000876Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:feb84b0c983354e634374aa2d88759d64e2caa246ba9b4b5d837e7fb28671791","observation_id":"1a1e7c04-c860-468a-92e4-3cbecc67fb3e","resolution":{"observed_at":"2026-08-01T17:08:10.000876Z","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":"2026-08-01T17:08:10.133832Z","title":"Mlperf inference benchmark,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T17:08:10.133832Z"},"links":{"citing_paper":"/paper/2607.17758"},"observation_digest":"sha256:957c2f630798f486806aa70ce3253477c2630b890934949267b1350f2824d030","observation_id":"440ba518-7dba-4962-94b4-58cd3bbe6496","resolution":{"observed_at":"2026-08-01T17:08:10.133832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.17758","last_updated":"2026-07-20T09:49:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T17:08:06.941211Z","submitted_at":"2026-07-20T09:49:26Z","title":"Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":32},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.17758."}