{"as_of":"2026-08-09T15:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2bccee0e015b555be3a1f1cfbfff4cf9cf47f4f8185d3bf03fa605e70aa459da","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T08:17:30.916639Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.19404/citation-record","integrity":"/paper/2607.19404/integrity","json":"/paper/2607.19404/citation-record.json","paper":"/paper/2607.19404"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T08:17:30.297178Z","title":"Autoformer: De- composition transformers with auto-correlation for long- term series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.297178Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:7eb2e83aa2bf19994c7a72b0cb36e5d65735c1195dadda16c97fefc575692755","observation_id":"4864aafc-a844-4946-9376-7ed71db93aee","resolution":{"observed_at":"2026-08-02T08:17:30.297178Z","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":"2026-08-02T08:17:30.381154Z","title":"iTransformer: Inverted transformers are effective for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.381154Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:0f7be5b05de2bef62c52710e074351764c498a80f144c7dc7b791baa96984d37","observation_id":"c5c064e9-ea2f-4281-9350-46b708e34dab","resolution":{"observed_at":"2026-08-02T08:17:30.381154Z","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":"2026-08-02T08:17:30.526883Z","title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.526883Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:156845c16ed6db957bb45e848bb54b7d0ceb6e8032f53e9b7ee25ddbfc42e4eb","observation_id":"f3b68d2c-c516-46b6-947b-cf58b2d1678d","resolution":{"observed_at":"2026-08-02T08:17:30.526883Z","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":"2026-08-02T08:17:30.645986Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.645986Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:fe57ae28de44e8fc569cb89f693d087d117bf334c30f170bc00b3be2e58cf93a","observation_id":"640eb8c6-4ace-4214-9e43-1171ad589229","resolution":{"observed_at":"2026-08-02T08:17:30.645986Z","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":"2026-08-02T08:17:30.670679Z","title":"TimeMixer: Decomposable multi- scale mixing for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.670679Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:c9bf1187b39b51d4498163e2a5b2f67a8444fdba4495db4be1983f4134efd346","observation_id":"98d1bdf3-7828-447a-aebf-e3530cf8f88b","resolution":{"observed_at":"2026-08-02T08:17:30.670679Z","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":"2026-08-02T08:17:30.675031Z","title":"Time series forecasting via direct per-step probability distribution modeling,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.675031Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:686a19d1914f506e3f7a72bcaf92f344d28b236cba08f2886b4ec464c28b42dc","observation_id":"251a3ac8-21c0-4b88-b6fd-fee1fb88b844","resolution":{"observed_at":"2026-08-02T08:17:30.675031Z","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":"2026-08-02T08:17:30.679812Z","title":"A riemannian network for spd matrix learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.679812Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:56e3fd70a76a83b833852262460df1546de84f3e2b6819ed135c89041e2cdbd1","observation_id":"c870083d-bd6a-4a0e-a07f-cfac24979336","resolution":{"observed_at":"2026-08-02T08:17:30.679812Z","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":"2026-08-02T08:17:30.684169Z","title":"Manifoldformer: Geomet- ric deep learning for neural dynamics on riemannian manifolds,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.684169Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:396d7faf14b972d86e5d5ad27650ff4d2a22aa597d405b7fb0eaa18fc9c1fec3","observation_id":"28f8ce06-7c5e-4105-a9e5-0beab952bf65","resolution":{"observed_at":"2026-08-02T08:17:30.684169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.09917","last_updated":"2026-06-06T17:04:57Z","snapshot_observed_at":"2026-08-08T00:43:56.145518Z","submitted_at":"2026-06-06T17:04:57Z","title":"SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.09917","snapshot_observed_at":"2026-08-02T08:17:30.688149Z","title":"Spdm: Geometry-modulated state space modeling with manifold constraints for time series forecasting,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.688149Z"},"links":{"cited_paper":"/paper/2606.09917","citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:c19d2cd898b61b6650668697fdfc3e6aa7e02165c1de5c4ce97ac2855724a59b","observation_id":"c8e9304d-203e-4d98-92d4-f19b5d6c699c","resolution":{"observed_at":"2026-08-02T08:17:30.688149Z","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":"2026-08-02T08:17:30.692620Z","title":"Mamba: Linear-time sequence mod- eling with selective state spaces,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.692620Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:c9e7b482ea0739f005f4e21d82c06cdd132de55ba44c1148c989130ac222ff3c","observation_id":"0c56c212-1533-461a-9e52-62dc24abc54c","resolution":{"observed_at":"2026-08-02T08:17:30.692620Z","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":"2026-08-02T08:17:30.696689Z","title":"Arima models,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.696689Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:c8b396a6331190a1e202b763dfdc345669ead4e6fcf2bbb1b28f58a1ce7b0252","observation_id":"dd8cb815-6c80-4384-8e00-3ed6057dbe51","resolution":{"observed_at":"2026-08-02T08:17:30.696689Z","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":"2026-08-02T08:17:30.700771Z","title":"Vector autoregressive models for multivariate time series,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.700771Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:88a7442aed5f2c45e0bff408ebf82fecef8f85a532e8d12a780545deddd894cd","observation_id":"fb3450a8-5735-48f4-b989-e6a4231bc881","resolution":{"observed_at":"2026-08-02T08:17:30.700771Z","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":"2026-08-02T08:17:30.704834Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.704834Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:a8a79e4ded45d18e9f8eba4bbf685800562627fc52cc40de679668a74dc26351","observation_id":"b7a387ac-755d-4c16-9e42-d9f594159e6d","resolution":{"observed_at":"2026-08-02T08:17:30.704834Z","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":"2026-08-02T08:17:30.708791Z","title":"Transformers in time series: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.708791Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:8a643cfed5c9f8a9e9f41c7d4bd4dd4dcb57a14a9b46a60de4b67770fcee62ba","observation_id":"2d48c4d0-7786-4c61-9e8b-fd8184ea5a9a","resolution":{"observed_at":"2026-08-02T08:17:30.708791Z","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":"2026-08-02T08:17:30.712735Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.712735Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:e79220b040e35f6b3ef69e613b766f817f0098e09acc8f248db910f458a0e43b","observation_id":"2853fac7-aefb-43e5-96c2-e2379820a6d6","resolution":{"observed_at":"2026-08-02T08:17:30.712735Z","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":"2026-08-02T08:17:30.716576Z","title":"FEDformer: Frequency enhanced decomposed trans- former for long-term series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.716576Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:abe0d50cd3de04e0182276c1fd32869441595887ba4873b178c5387a90faf235","observation_id":"de7bbbba-4528-4396-9ae6-47c010db9227","resolution":{"observed_at":"2026-08-02T08:17:30.716576Z","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":"2026-08-02T08:17:30.720359Z","title":"Are transform- ers effective for time series forecasting?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.720359Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:ca04e56611aeec00168543a7e6bf4d315984471f8ddb1e5f0d7f69b1c4b019ca","observation_id":"093ece28-f6db-479e-9d55-952270b49438","resolution":{"observed_at":"2026-08-02T08:17:30.720359Z","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":"2026-08-02T08:17:30.724220Z","title":"Long-term forecasting with TiDE: Time-series dense encoder,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.724220Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:a003f941a328b610c566cb980fad073820568d7f9197233fc77e40cd479da58d","observation_id":"40ed8654-82d4-4d2e-9073-26099e2f46b8","resolution":{"observed_at":"2026-08-02T08:17:30.724220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-07-06T06:26:27.965096Z","submitted_at":"2018-03-04T00:20:29Z","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.01271","snapshot_observed_at":"2026-08-02T08:17:30.728021Z","title":"An empirical evalua- tion of generic convolutional and recurrent networks for sequence modeling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.728021Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:7ada2ab1825896b32766a3f25cca34e45e299b903c51907926d49391381688f0","observation_id":"162493fb-4194-4202-ac77-0931cc093607","resolution":{"observed_at":"2026-08-02T08:17:30.728021Z","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":"2026-08-02T08:17:30.732718Z","title":"ModernTCN: A modern pure convolution structure for general time series analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.732718Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:5efd7e452576c979d7f38d04194c6e9e764eb1d84198c34fba83106913178864","observation_id":"2f36ab1b-51a9-4db7-a063-443e9ab58f2b","resolution":{"observed_at":"2026-08-02T08:17:30.732718Z","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":"2026-08-02T08:17:30.736939Z","title":"Deep time series models: A comprehensive survey and benchmark,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.736939Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:f8c3d90165c61a58953b9b04928cad52623f7014a2d70ee48f105e4e34c476da","observation_id":"98347df6-693c-4966-a1cf-f0de30f5d037","resolution":{"observed_at":"2026-08-02T08:17:30.736939Z","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":"2026-08-02T08:17:30.740932Z","title":"Ctfnet: Long-sequence time-series forecasting based on convolution and time–frequency analysis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.740932Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:3f372f054894b7d6eb579e1629f380d0d73dd1df88a93b7b07504b769739dc0a","observation_id":"f253db42-5ce6-4005-95f1-3d6fccf062fa","resolution":{"observed_at":"2026-08-02T08:17:30.740932Z","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":"2026-08-02T08:17:30.744730Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.744730Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:17f2c538bbb8f36869d42c93784e87b2ca06cbe3f8b6ccf9ef222958f54d43df","observation_id":"50a4e520-ca2b-4e41-acca-c38dbdb22996","resolution":{"observed_at":"2026-08-02T08:17:30.744730Z","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":"2026-08-02T08:17:30.752782Z","title":"Periodpatch: A frequency-aware modular framework with patch-based embedding and periodic bias for multivariate time series forecasting,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.752782Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:aeb86693970bdb64bfaf56d5307e5d2b2a35c1f2804f92bca2ba34b135a2b577","observation_id":"7a3aebd5-6d7f-439f-879c-1c85ee9bf788","resolution":{"observed_at":"2026-08-02T08:17:30.752782Z","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":"10.1609/aaai.v40i29.39610","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Recast: Reliability-aware codebook-assisted lightweight time series forecasting,","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"f554ab77-7bd8-4627-a3f3-ea79fe10eb6c","year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.757299Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:03f99125d7b985bb5d1b4a18e0b9923b0822868928d66ccc02978f94e2d5d274","observation_id":"71c9189a-5f01-4f1d-815c-e9c8649e08c7","resolution":{"observed_at":"2026-08-02T08:18:23.326232Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-02T08:17:30.761328Z","title":"Gaussian adaptive patching powered fully- connected spatial-temporal graph for multivariate time- series data,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.761328Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:ba141eb05456563e84ebe67227c625aedbc5fd072cb618d50368ddb47add5c34","observation_id":"171ea095-06e8-4359-be2a-88315b497137","resolution":{"observed_at":"2026-08-02T08:17:30.761328Z","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":"2026-08-02T08:17:30.765141Z","title":"Dish-ts: A general paradigm for alleviating distribution shift in time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.765141Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:f572a80439c179ee065807dca8a5b2121b8cfef1732268e94ea1bf6468e4a186","observation_id":"7578664b-baba-458c-b5df-c29d1326cd18","resolution":{"observed_at":"2026-08-02T08:17:30.765141Z","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":"2026-08-02T08:17:30.878562Z","title":"Reversible instance normalization for accurate time-series forecasting against distribution shift,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.878562Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:4939e976345dc2c414c3b27fc10add224f7c36ad9053ea1844d2b726e3242ba5","observation_id":"f14e04d5-994b-49d4-9738-936a7b2d1bb6","resolution":{"observed_at":"2026-08-02T08:17:30.878562Z","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":"2026-08-02T08:17:30.882776Z","title":"Koopman neural operator forecaster for time-series with temporal distributional shifts,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.882776Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:fb113056f3415d71e8449da6cac77f06a98903b9538b841a908e229e34010d85","observation_id":"d912f117-8620-42b1-b87e-95c847fb8130","resolution":{"observed_at":"2026-08-02T08:17:30.882776Z","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":"2026-08-02T08:17:30.886806Z","title":"Disents: Disentangled channel evolving pattern modeling for multivariate time series forecasting,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.886806Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:f0eb4e3eff47f3cdbe2907d1b6368fc9433681ef38773578e4f2bd3138a52fd3","observation_id":"957d1928-aa70-4457-b08f-26b1da366bef","resolution":{"observed_at":"2026-08-02T08:17:30.886806Z","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":"2026-08-02T08:17:30.890855Z","title":"Generalized dimension-reduction framework for recent-biased time series analysis,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.890855Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:7ee58e6d935c075f297a5b0dda3cd2971954839f66e05100ae7d7a8efe6a0fcb","observation_id":"bfc75e84-39cf-4570-82f1-39964e8d6b3c","resolution":{"observed_at":"2026-08-02T08:17:30.890855Z","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":"2026-08-02T08:17:30.894828Z","title":"Time-series classification with cote: the collective of transformation-based ensembles,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.894828Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:80c71c99e8caf5d9ae6f08700607ecb4909f16394bd01456837e1b0dd5e72a12","observation_id":"55b0705c-4fb5-4a21-b22a-e6bcf4582419","resolution":{"observed_at":"2026-08-02T08:17:30.894828Z","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":"2026-08-02T08:17:30.899288Z","title":"Multivariate time-series classification using the hidden-unit logistic model,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.899288Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:ade8aa959cdd6a3b830a771de66ebb21a3c2993635b15ab86fc0d5c942fb50d1","observation_id":"c2e872d6-38bd-4c99-bb0f-b8a715cb8d92","resolution":{"observed_at":"2026-08-02T08:17:30.899288Z","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":"2026-08-02T08:17:30.903581Z","title":"Is mamba effective for time se- ries forecasting?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.903581Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:f0c6a5bbc3d9541f7102d75df705eca773ff119186c63baa8e2741bf69073837","observation_id":"d381cf91-4049-4f86-9a31-b47043c0f34d","resolution":{"observed_at":"2026-08-02T08:17:30.903581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-02T08:17:30.907311Z","title":"Umap: Uniform manifold approximation and projection for dimension reduction,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.907311Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:9a960d1ff3fbe6bd08f2bee0f043b3bbc162ce1f6255ba5b7bdbde5fd087d838","observation_id":"1dfdba19-5751-47eb-84b1-cf5d6beaab53","resolution":{"observed_at":"2026-08-02T08:17:30.907311Z","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":"2026-08-02T08:17:30.912073Z","title":"Some methods for classification and analysis of multivariate observations,","venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.912073Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:b2609120f76702e8f372ab077b173acec7f360bc95c51cf78fd4ae32ddd03524","observation_id":"84be1d5a-52c0-4ea5-b3c2-58b6514d727d","resolution":{"observed_at":"2026-08-02T08:17:30.912073Z","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":"2026-08-02T08:17:30.916639Z","title":"Comparing partitions,","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.916639Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:a8a8871b6094c37af1ce646e772bb49f2642ebdbab495f8e70529052215acf6f","observation_id":"245c17f5-201f-4ed1-9e68-2795e87e4801","resolution":{"observed_at":"2026-08-02T08:17:30.916639Z","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":"2026-08-02T08:17:30.748730Z","title":"Available: https://openreview.net/forum? id=ju Uqw384Oq","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T08:17:30.748730Z"},"links":{"citing_paper":"/paper/2607.19404"},"observation_digest":"sha256:f17c606e5fff89cc99ba6b4b7031863894d0b3368f77dba8b92ca3e305a3d2f1","observation_id":"ba34f40e-631f-4349-8efc-b96af140d7f5","resolution":{"observed_at":"2026-08-02T08:17:30.748730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.19404","last_updated":"2026-07-07T19:19:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T02:48:18.642492Z","submitted_at":"2026-07-07T19:19:14Z","title":"Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":38},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.19404."}