{"as_of":"2026-08-18T16:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de2f76c682ddd4c3717a7de34e1dc5a0fa1176239c9cd21d6b8f5e65e415ca4f","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:24:44.917173Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2508.21330/citation-record","integrity":"/paper/2508.21330/integrity","json":"/paper/2508.21330/citation-record.json","paper":"/paper/2508.21330"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.01514","last_updated":"2018-07-04T10:55:38Z","snapshot_observed_at":"2026-08-18T14:27:46.730025Z","submitted_at":"2018-07-04T10:55:38Z","title":"Generating Synthetic but Plausible Healthcare Record Datasets","version":1},"cited_work":{"arxiv_id":"1807.01514","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.01514","snapshot_observed_at":"2026-08-05T14:24:45.051456Z","title":"Generating Synthetic but Plausible Healthcare Record Datasets","venue":"stat.ML","work_id":"cf61da87-cbca-4558-b8fe-3f2002e266ee","year":2018},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.760412Z"},"links":{"cited_paper":"/paper/1807.01514","citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:abb25c6967a4d773ac218ab4a6ffc9d775708f83f96fd257d33afcb5e0530bcb","observation_id":"eb27ac27-e8fd-465a-a168-4ab17dcfd33d","resolution":{"observed_at":"2026-08-05T14:24:45.060410Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.885562Z","title":"Generative adversarial networks in time series: A systematic literature review.ACM Computing Surveys, Conference’17, July 2017, Washington, DC, USA Hou X., Liu S","venue":null,"work_id":"79eddfa8-2a82-4ab3-b2d6-62609dfbb39c","year":2017},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.816875Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:ab187e6206200af9689117b712e82ec84028646a2da39f2bfb5b9c5db456eb20","observation_id":"15dcb6ba-b0d5-4d57-bb11-54321073cb5d","resolution":{"observed_at":"2026-08-05T14:24:45.895436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.848101Z","title":"On the constrained time-series generation problem","venue":null,"work_id":"ddf989ca-f9d3-4a94-af72-508c1e89761f","year":2024},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.842096Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:bab99d414f08ec22b8ed2105c5ba076344553025f56aa728bbd0f556b7e93bc5","observation_id":"506815af-d5d3-4704-a461-64fb151d89a6","resolution":{"observed_at":"2026-08-05T14:24:45.859852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08095","last_updated":"2021-12-07T09:00:04Z","snapshot_observed_at":"2026-08-16T17:41:38.448202Z","submitted_at":"2021-11-15T21:42:14Z","title":"TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08095","snapshot_observed_at":"2026-08-05T14:24:43.899508Z","title":"Timevae: A variational auto-encoder for multivariate time series generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.899508Z"},"links":{"cited_paper":"/paper/2111.08095","citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:02dec24b149094f5b3a751973264cfb3c91f434e148ea3861526ba3eaf7b566e","observation_id":"08656db0-53bc-4d2f-8ce5-8906c31a0d48","resolution":{"observed_at":"2026-08-05T14:24:43.899508Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.792611Z","title":"Diffit: Diffusion vision transformers for image generation","venue":null,"work_id":"11e748cb-264a-4b23-8597-671e4a4da399","year":2025},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.943213Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:07d978903cd9fa8adc6ed61e3ae441111adffca911e13502012ac4d2cadac3bc","observation_id":"429d3159-ac73-409d-a6f9-a602fcd6f792","resolution":{"observed_at":"2026-08-05T14:24:45.803227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.750277Z","title":"Psa-gan: Progressive self attention gans for synthetic time series","venue":null,"work_id":"79ff9ff4-06c5-4e8a-93bb-23648fae9818","year":2022},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:43.990600Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:6bdc704ce282612b14be79a28956db97efbf8239fe672cbaece02c699fc8d568","observation_id":"5221d973-f802-4132-9a40-2e1a05c3dfec","resolution":{"observed_at":"2026-08-05T14:24:45.760675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.716432Z","title":"Gt-gan: General purpose time series synthesis with generative adversarial networks","venue":null,"work_id":"0e62ca52-f32d-40bc-8bd5-6c35539d2283","year":2022},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.035028Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:cc56d1e3d3be9c7dff2b05ee21e6ad636b9a93f35981af5196f5fb68fb895d08","observation_id":"2014525a-7a9f-41d8-9bb3-4429e627591f","resolution":{"observed_at":"2026-08-05T14:24:45.723875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.680585Z","title":"Dif- fwave: A versatile diffusion model for audio synthesis","venue":null,"work_id":"38a158ff-33ce-44e1-b668-099a914e59c9","year":2020},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.115582Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:0c37a29fe1f648acf78a0bb2e475a31b63c8040438846d466c178254d57b9cc5","observation_id":"a4f4f6c2-3416-4e14-aee3-8051fde92143","resolution":{"observed_at":"2026-08-05T14:24:45.688571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.145480Z","title":"Modeling long-and short-term temporal patterns with deep neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.145480Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:310658bb8e10666f6453b86efcfcf7365cd6f4c655c752a687b0f3bde0b9e1c2","observation_id":"98bfaa80-cc14-4fd7-b1b5-cfe13074b90c","resolution":{"observed_at":"2026-08-05T14:24:44.145480Z","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":"2026-08-05T14:24:44.207029Z","title":"Causal recurrent variational autoencoder for medical time series generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.207029Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:70cafb2c7c1cf946b8e1d916a58d323e1fb8971ade82f412d351339cf417c235","observation_id":"36032e94-8da6-49d8-9a7f-e6555df5dbc5","resolution":{"observed_at":"2026-08-05T14:24:44.207029Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.589922Z","title":"Generative time series forecasting with diffusion, denoise, and disentanglement","venue":null,"work_id":"8e9b20bb-5b9b-4130-9027-44ce7f1359c3","year":2022},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.298076Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:12c68e6c3abfb8ffa5d20506bec5f9ae053a7fb5e07d8696d58f4455ad9a36a0","observation_id":"dd9938aa-a306-4e29-944f-b5f3e9212396","resolution":{"observed_at":"2026-08-05T14:24:45.597904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.318724Z","title":"itransformer: Inverted transformers are effective for time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.318724Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:a18f206c30217fdbcc018e0a89859994bc7fd0967d443ab8c4a36d9516f115d1","observation_id":"2d21c9b0-4107-4ba1-a449-740e4c340cd3","resolution":{"observed_at":"2026-08-05T14:24:44.318724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.09904","last_updated":"2016-11-29T21:53:09Z","snapshot_observed_at":"2026-08-18T14:23:50.403394Z","submitted_at":"2016-11-29T21:53:09Z","title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.09904","snapshot_observed_at":"2026-08-05T14:24:44.326616Z","title":"C-rnn-gan: Continuous recurrent neural networks with adversar- ial training","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.326616Z"},"links":{"cited_paper":"/paper/1611.09904","citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:e76219f41f8ed9c7778cdbaa52ef6e4e8e209f2c7e3134f0a9f7f9c72fc2a28b","observation_id":"f68c5a63-1aa9-45ff-8102-52f1b03540e7","resolution":{"observed_at":"2026-08-05T14:24:44.326616Z","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":"2026-08-05T14:24:44.356973Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.356973Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:0ab3b6d5ae9a51bcd3f113135aadaeab3293c9b6f1e86c97e7593051e7b0eb48","observation_id":"00dd64f5-302f-4d8f-9d09-3621d0b6ec34","resolution":{"observed_at":"2026-08-05T14:24:44.356973Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.499439Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":"e617ce55-8413-4984-a445-4ceb7ae4f753","year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.417818Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:444ffbfbe46fbbeaaa0542c062efeb0690feb231e5c4b79db99076f732bcaaad","observation_id":"56c180e5-e3b8-4740-aa59-83d4a3617d89","resolution":{"observed_at":"2026-08-05T14:24:45.506922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.461141Z","title":"Scale- former: Iterative multi-scale refining transformers for time series forecasting","venue":null,"work_id":"327c9c6d-bbdf-44fd-b1c9-99f0bab0ba70","year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.480402Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:d97c18fccfdaa636c626b7854fc8d329624fa8fdf1a0268ab8e30032fc9f9e41","observation_id":"6b2c89d7-e2a4-49c5-aaa5-a682a8930568","resolution":{"observed_at":"2026-08-05T14:24:45.476140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.429269Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":"7d6f9cbb-54b3-49f8-94ad-10bba9a2928c","year":2015},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.541426Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:14335df4a8128484b953c5a8b1eb1aafba58222cec3f187654505675e1c45772","observation_id":"123fd03c-6212-442c-ac45-7fcd8b0a4268","resolution":{"observed_at":"2026-08-05T14:24:45.442077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.387518Z","title":"Csdi: Conditional score-based diffusion models for probabilistic time series imputation","venue":null,"work_id":"d0a09d16-4d00-4079-b146-a9dc77964164","year":2021},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.598705Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:f68ab78b38e0d7e0da1e6582e82f602030c12157fa25b5806bac6cf6457bd338","observation_id":"4003e882-b4ea-4d2f-85b7-ce593b994d4f","resolution":{"observed_at":"2026-08-05T14:24:45.396326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.650689Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.650689Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:d017340fc7bf18469977658c3aa0d29085942fa067ab84f7be6c3dc2c41dd470","observation_id":"22cdcfd6-3ace-493e-b343-28edf1bd73ac","resolution":{"observed_at":"2026-08-05T14:24:44.650689Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.335953Z","title":"Aec-gan: adversarial error correction gans for auto-regressive long time-series generation","venue":null,"work_id":"e1038693-3dc3-494d-a629-2b850596d622","year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.753098Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:95c2e1de7b173683ef6437bf6e965e610319f54f362fc5ff29dbf943a045b5ab","observation_id":"48d64a38-7cb2-4470-80d9-62c23a6a1c99","resolution":{"observed_at":"2026-08-05T14:24:45.345078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.816565Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.816565Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:687e2359570431118a787994767754d984c6ba881e9d03d52df751e34f6e3605","observation_id":"fbeb970d-61c5-4df5-b8d6-b091469afcc6","resolution":{"observed_at":"2026-08-05T14:24:44.816565Z","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":"2026-08-05T14:24:44.837557Z","title":"Autoformer: Decom- position transformers with auto-correlation for long-term series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.837557Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:0690e288cb39c180d45aef458d676e9216a8010cd59fc6935b68659cb32b5753","observation_id":"c5523d24-b662-462e-82dd-cdb3bdec796d","resolution":{"observed_at":"2026-08-05T14:24:44.837557Z","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":"2026-08-05T14:24:44.844534Z","title":"Time-series generative adversarial networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.844534Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:a46d25375f382446af433ba8a1df7992b5147383c63b63ac7fb1f367848e51de","observation_id":"d118d2b5-9730-4977-bf47-d6f1a30782a3","resolution":{"observed_at":"2026-08-05T14:24:44.844534Z","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":"2026-08-05T14:24:44.860416Z","title":"Diffusion-ts: Interpretable diffusion for general time series generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.860416Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:9b29c63d74f6a297c48f1991aa0439c82ba41eb789786ef9603b9e4020180e8c","observation_id":"e8f0f0db-9881-4b49-b0bf-9b2ff4c5cc0e","resolution":{"observed_at":"2026-08-05T14:24:44.860416Z","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":"2026-08-05T14:24:44.868390Z","title":"Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.868390Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:4235817f5a8ce23f3c759b1ceb22ee68500fd5d613f45a8236714aa7a5c579e8","observation_id":"75ac2265-cd70-4ffd-8ec4-62eabb031b1b","resolution":{"observed_at":"2026-08-05T14:24:44.868390Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.182601Z","title":"A transformer-based framework for multivariate time series representation learning","venue":null,"work_id":"95e631a4-7409-426a-8c46-007ea543e8a7","year":2021},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.875001Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:6fb20fe3c6c9a9d323f0293c213bcbf2d3efb1a998b12c419ca5d9a7bb543c76","observation_id":"91fac19a-e8df-43cc-b849-d851ea49738f","resolution":{"observed_at":"2026-08-05T14:24:45.190371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.882474Z","title":"Privbayes: Private data release via bayesian networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.882474Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:df12741ff059f7c7ca8b2b5e4dab2dfbb1914ffa7da2f171b814b789406104cc","observation_id":"2a4cfd38-c867-427d-80e3-fe9ab2838b86","resolution":{"observed_at":"2026-08-05T14:24:44.882474Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.137862Z","title":"Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting","venue":null,"work_id":"444865ee-10e5-42d8-b28c-da89d797b972","year":2023},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.891245Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:bb57efd35501ec244a7dc8ed579bc7127bebb158bd3c6b8b81061079aacab0ae","observation_id":"da942f7a-ba42-47aa-afb3-5c320accc9bd","resolution":{"observed_at":"2026-08-05T14:24:45.146905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:24:45.105099Z","title":"Sageformer: Series-aware framework for long-term multivariate time series forecasting","venue":null,"work_id":"90617e51-fabe-4830-8710-152be8f8f0f0","year":2024},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.906812Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:fcb200cd2c34f47b4f0254e692e27cd3298c04581968043dcb9c92bd0a43a3f7","observation_id":"8798bdd2-c1f4-44ef-a2fc-1bb6503d89ba","resolution":{"observed_at":"2026-08-05T14:24:45.112824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-05T14:24:44.917173Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T14:24:44.917173Z"},"links":{"citing_paper":"/paper/2508.21330"},"observation_digest":"sha256:eefad0c4cbb6eebd344208239317577266f6da4502126674a26224fe3334e028","observation_id":"1fbe97c3-0a25-4b7d-9e76-62c3e9a2a8cd","resolution":{"observed_at":"2026-08-05T14:24:44.917173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.21330","last_updated":"2025-08-29T05:10:10Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T14:27:06.653613Z","submitted_at":"2025-08-29T05:10:10Z","title":"Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":30},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2508.21330."}