{"as_of":"2026-08-19T19:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:80c0dfe28a78fa5615cf0d3f64e847762337b83419c099d483063311139ec2b5","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T07:50:39.960612Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2607.21681/citation-record","integrity":"/paper/2607.21681/integrity","json":"/paper/2607.21681/citation-record.json","paper":"/paper/2607.21681"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T07:50:36.766092Z","title":"In: Proc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:36.766092Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:af3d0984d4448f3465cf6728401b3b2b86094daab9b01791ccab098e850f5bf3","observation_id":"95253237-0ca1-4c5b-bb9c-57c6c8956cb1","resolution":{"observed_at":"2026-08-01T07:50:36.766092Z","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":"2026-08-01T07:50:36.835753Z","title":"In: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:36.835753Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:31276317edef0e6b1fd1e8dda2fa90b961a07baaa1344b608df871fb2bcb1acb","observation_id":"b9da2cf3-900f-4f12-b215-5e9385a5ad24","resolution":{"observed_at":"2026-08-01T07:50:36.835753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.08022","last_updated":"2017-11-06T14:21:43Z","snapshot_observed_at":"2026-08-15T14:21:42.814056Z","submitted_at":"2016-07-27T10:23:00Z","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.08022","snapshot_observed_at":"2026-08-01T07:50:36.916757Z","title":"arXiv preprint arXiv:1607.08022 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:36.916757Z"},"links":{"cited_paper":"/paper/1607.08022","citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:81441323b962b0b20b7e808a8a4a90769ff6e8f90eae32eb5228ca94a7fa764a","observation_id":"378359f8-0fff-4a85-b63a-a8896f9addd6","resolution":{"observed_at":"2026-08-01T07:50:36.916757Z","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":"2026-08-01T07:50:36.984697Z","title":"In: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:36.984697Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:3fc07970fd0bb63893779df6935b851673480395993256a7a07b2705c39235d3","observation_id":"21aa533b-b9ba-41ab-8394-a2a117e93020","resolution":{"observed_at":"2026-08-01T07:50:36.984697Z","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":"2026-08-01T07:50:37.036790Z","title":"et al.: BigBird: Transformers for Longer Sequences","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.036790Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:6fd0215dfccab67985d0fcd57d620181f39791bb2474a15f6c114fc146022e10","observation_id":"8ecd56d3-98ec-407f-9045-3f41b04e7566","resolution":{"observed_at":"2026-08-01T07:50:37.036790Z","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":"2026-08-01T07:50:37.154372Z","title":"et al.: Informer: Beyond Efficient Transformer for Long Sequence Time- Series Forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.154372Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:27664eabb1ec384f909d5b6d616b328130f00965fd264fb8c208dce0bf380caa","observation_id":"8af1a906-3b8b-458f-9b1c-b95ff4804215","resolution":{"observed_at":"2026-08-01T07:50:37.154372Z","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":"2026-08-01T07:50:37.250905Z","title":"In: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.250905Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:6ad28fe7761f32c2331bd3e3256acae31b458688c0c9eaa1998c7c3395a1b9d6","observation_id":"7666945d-cbb1-42a3-b1a5-4ae54b2db7f3","resolution":{"observed_at":"2026-08-01T07:50:37.250905Z","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":"2026-08-01T07:50:37.326008Z","title":"et al.: Is Mamba Effective for Time Series Forecasting? Neurocomputing (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.326008Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:944f9182239a0bce0d885cc02384848bb4d22970145597419666a31cf1bad294","observation_id":"681286e9-8b6a-44ea-a4ee-e181238023c0","resolution":{"observed_at":"2026-08-01T07:50:37.326008Z","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":"2026-08-01T07:50:37.405179Z","title":"et al.: TimePro: Efficient Multivariate Long-term Time Series Forecast- ing with Variable- and Time-Aware Hyper-state","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.405179Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:1f53e3fc95f4144a9298519aa56f1e2b72a03aa4358873fec87577b29afa20b5","observation_id":"d314a107-2d2f-4333-8e4c-f5a5c319a90c","resolution":{"observed_at":"2026-08-01T07:50:37.405179Z","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":"2026-08-01T07:50:37.478333Z","title":"et al.: Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.478333Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:26778bda6a9322356e54133b8a58cf730db40770af3bea1ca6d29c74383af5e6","observation_id":"e25f8400-019d-4703-91b2-132501b54b0f","resolution":{"observed_at":"2026-08-01T07:50:37.478333Z","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":"2026-08-01T07:50:37.558510Z","title":"et al.: FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.558510Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:2d19ee46968ce19f9cfb62ab92890d5115db8898fba4047ad616466d0b7660ad","observation_id":"054e6cee-3863-4316-9735-41d6cfe3a359","resolution":{"observed_at":"2026-08-01T07:50:37.558510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09364","last_updated":"2023-12-11T15:46:13Z","snapshot_observed_at":"2026-08-16T15:24:05.021906Z","submitted_at":"2023-06-14T06:26:23Z","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09364","snapshot_observed_at":"2026-08-01T07:50:37.636156Z","title":"et al.: TSMixer: An All-MLP Architecture for Time Series Forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.636156Z"},"links":{"cited_paper":"/paper/2306.09364","citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:5280190a1147dee85fd4eaf3dd679fb5a5938b0d51be0e769934d34c1767e3ed","observation_id":"9e99f009-5dfe-441d-9cb8-45d813f2c4b3","resolution":{"observed_at":"2026-08-01T07:50:37.636156Z","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":"2026-08-01T07:50:37.809916Z","title":"et al.: SOFTS: Series-Core Fusion Transformer for Multivariate Time Series Forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.809916Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:5352c776c7818ad59e86955906f78091ab453b2b008ae1e019be19afa766232f","observation_id":"ffd6b796-0349-41df-a8f8-9d6c9ebdd0c2","resolution":{"observed_at":"2026-08-01T07:50:37.809916Z","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":"2026-08-01T07:50:37.963878Z","title":"et al.: Graph WaveNet for Deep Spatial-Temporal Graph Modeling","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:37.963878Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:7588e62e7833d9ec35befdd213911393f136f34fb8f2a61d5ec1dafbbdd10f55","observation_id":"db0546f5-dce2-4872-9ca2-96fb37328bf4","resolution":{"observed_at":"2026-08-01T07:50:37.963878Z","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":"2026-08-01T07:50:38.080690Z","title":"et al.: SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.080690Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:f3524aa8eabf89800bf7c7901678b6f0a4aac60c42109c0bbedb39631199ad9a","observation_id":"7c461809-43ce-4e5f-b103-6c23e883d55a","resolution":{"observed_at":"2026-08-01T07:50:38.080690Z","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":"2026-08-01T07:50:38.219218Z","title":"et al.: CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.219218Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:f4401723bd18033b83302b9f71817e54e2b0087481bccdb430409251a03096c1","observation_id":"45ee486e-591f-445d-bdcc-59b50cf3f425","resolution":{"observed_at":"2026-08-01T07:50:38.219218Z","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":"2026-08-01T07:50:38.301940Z","title":"et al.: Temporal Query Network for Efficient Multivariate Time Series Forecasting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.301940Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:ebe735e9d88172d10809fecbdc7478b0358054dbeca984bc3241ed4e3657ef87","observation_id":"e3d87cbc-6338-4ab1-ab4f-905aa74527c7","resolution":{"observed_at":"2026-08-01T07:50:38.301940Z","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":"2026-08-01T07:50:38.444995Z","title":"et al.: Are Transformers Effective for Time Series Forecasting? In: Proc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.444995Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:6e77ed9298a2d3663bbaef4c965335cc7a8bd226919ed05ae2061a91dadc2284","observation_id":"e33932d9-f1e0-4d96-a4fa-429c40b6dbb1","resolution":{"observed_at":"2026-08-01T07:50:38.444995Z","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":"2026-08-01T07:50:38.591614Z","title":"et al.: TimeMixer: Decomposable Multiscale Mixing for Time Series Fore- casting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.591614Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:fdac6082aee8802393470b26194cf5a8374d5341ff963cc6d9b6f30cc5bdef90","observation_id":"124ca14b-45ac-48ca-996d-b901b36b9f6f","resolution":{"observed_at":"2026-08-01T07:50:38.591614Z","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":"2026-08-01T07:50:38.700454Z","title":"et al.: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.700454Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:bedb6ac3f1e81a488939390bb555c1d2248b9018337d59b8c62c880709826af2","observation_id":"8665769c-f677-4933-82e4-9f68a628a065","resolution":{"observed_at":"2026-08-01T07:50:38.700454Z","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":"2026-08-01T07:50:38.817124Z","title":"et al.: PyTorch: An Imperative Style, High-Performance Deep Learning Library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.817124Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:8f0f7a9ae465b7ce6f33e3d681503ef14ebedbe17e0cb666898b00fffa649024","observation_id":"57627d37-f54d-4134-97c9-e4f6d5367453","resolution":{"observed_at":"2026-08-01T07:50:38.817124Z","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":"2026-08-01T07:50:38.934432Z","title":"In: Proc","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:38.934432Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:731231024e049a8db0894335dbd3fe67b5f13ca18cbab5690cf37233e5e672c0","observation_id":"43c864e4-9188-4426-ab55-871bcc4fcaff","resolution":{"observed_at":"2026-08-01T07:50:38.934432Z","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":"2026-08-01T07:50:39.021025Z","title":"et al.: Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.021025Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:07d1149bab687a56ba9fbe86d8bbf8da7008b42b6cf44f8239f2a018f0bf8eeb","observation_id":"55d07722-1fec-418a-8c21-c63786fb2ab2","resolution":{"observed_at":"2026-08-01T07:50:39.021025Z","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":"2026-08-01T07:50:39.118178Z","title":"et al.: TiDE: Long-term Forecasting with Time-series Dense Encoder","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.118178Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:ddec4f5ac170f2f4d9541b1cda406d905137786460065e40dfdaaae4ebc778a8","observation_id":"9c01f317-bec6-44c3-acfd-45bdfa4f68e4","resolution":{"observed_at":"2026-08-01T07:50:39.118178Z","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":"2026-08-01T07:50:39.165920Z","title":"et al.: SCINet: Time Series Modeling and Forecasting with Sample Convo- lution and Interaction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.165920Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:2423e9ae2838ef3d594ca69cdc1037db43871ca3a4f07792f27ccb062b2aa418","observation_id":"05675c59-69b0-4b05-8e4e-6b5501d1a442","resolution":{"observed_at":"2026-08-01T07:50:39.165920Z","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":"2026-08-01T07:50:39.275626Z","title":"et al.: Modeling Long- and Short-Term Temporal Patterns with Deep Neu- ral Networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.275626Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:5f796ba07e81d438be98224e2d549482d60b96f2dfaa5df724a9aeae012d04b1","observation_id":"a8def415-6726-4928-84ac-9f2edd8442cf","resolution":{"observed_at":"2026-08-01T07:50:39.275626Z","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":"2026-08-01T07:50:39.380616Z","title":"et al.: TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.380616Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:07e0a1b007f476b3b8c527b5be152522d8dedd69c5b72e80816bf86c66971f9b","observation_id":"bb1e5786-de92-424e-ac72-0219d34c2a14","resolution":{"observed_at":"2026-08-01T07:50:39.380616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13278","last_updated":"2026-05-04T08:07:42Z","snapshot_observed_at":"2026-08-12T17:39:58.835778Z","submitted_at":"2024-07-18T08:31:55Z","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13278","snapshot_observed_at":"2026-08-01T07:50:39.484871Z","title":"et al.: Deep Time Series Models: A Comprehensive Survey and Bench- mark","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.484871Z"},"links":{"cited_paper":"/paper/2407.13278","citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:b033f247d3e3fcf16ea6260add972916a30aadbf44cec4e03d99d53870e5632c","observation_id":"c7bdb255-cd54-40d4-b47d-63bc26861563","resolution":{"observed_at":"2026-08-01T07:50:39.484871Z","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":"2026-08-01T07:50:39.587989Z","title":"et al.: Transformers in Time Series: A Survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.587989Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:2fa2e507ed33e3cd89a1005416b88ce2a778ad1e60d2b15624d3b9c8ea98662e","observation_id":"bc750b9c-f99e-47bc-bd1f-e70c9bc4a5e4","resolution":{"observed_at":"2026-08-01T07:50:39.587989Z","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":"2026-08-01T07:50:39.666285Z","title":"et al.: Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.666285Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:609b85430abca3f2d4fb5b79635f7922e82b88431a09bff63b252f5d8f022a0a","observation_id":"56244bff-f423-48aa-b00e-278c2a271df6","resolution":{"observed_at":"2026-08-01T07:50:39.666285Z","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":"2026-08-01T07:50:39.753374Z","title":"et al.: Transformers are RNNs: Fast Autoregressive Transform- ers with Linear Attention","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.753374Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:af5f154b80b70e5f69ef91d681e4800fc30d0fba327b188d942fa158ba799a4d","observation_id":"fd4d9ed0-124b-424e-ae5f-e3cff42e104a","resolution":{"observed_at":"2026-08-01T07:50:39.753374Z","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":"2026-08-01T07:50:39.845098Z","title":"Stochastic Pooling for Regularization of Deep Con- volutional Neural Networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.845098Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:63e373b3c8b45158f908fcdc4e8f6698b052a83465248f8b9acc3fcb92283f7f","observation_id":"480c9dca-3587-4cc4-a748-a5db20ad309a","resolution":{"observed_at":"2026-08-01T07:50:39.845098Z","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":"2026-08-01T07:50:39.915037Z","title":"CRC Press (2007)","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.915037Z"},"links":{"citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:2b8e99d58042e9a5170edd6a776314ebb92b8d50fb4a4c63df85cb625802cb59","observation_id":"584812de-2afc-4d41-a222-cd6b1e1b929e","resolution":{"observed_at":"2026-08-01T07:50:39.915037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-08-13T19:48:28.322536Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-01T07:50:39.960612Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T07:50:39.960612Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2607.21681"},"observation_digest":"sha256:e8220bca1c06d84e8c8134631ef2e10740fa55918fecd84fff807e47ce67ad7b","observation_id":"a299bfc4-859a-4c72-8f6d-6c0151fe6548","resolution":{"observed_at":"2026-08-01T07:50:39.960612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21681","last_updated":"2026-07-23T13:47:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T22:30:41.154089Z","submitted_at":"2026-07-23T13:47:02Z","title":"CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":34},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.21681."}