{"as_of":"2026-08-14T16:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45fc68285f3ef3c1cc0c8bd5a7f4d5b7d95419dff1b362d312d5200f65a4cd3a","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:55:54.227868Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:55:54.171229Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.10324","snapshot_observed_at":"2026-08-04T17:55:54.171229Z","title":"The block diagram is arXiv:2509.10324v1 [cs.LG] 12 Sep 2025 shown in Fig 1","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.171229Z"},"links":{"cited_paper":"/paper/2509.10324","citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:31d64972b57bf9a2ff73dc2d956a72d72b85d88d29d18166b2ffb3bf0b5ae2ee","observation_id":"9b497da5-37e8-4bec-9e1e-f8ab71864b40","resolution":{"observed_at":"2026-08-04T17:55:54.171229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.10324/citation-record","integrity":"/paper/2509.10324/integrity","json":"/paper/2509.10324/citation-record.json","paper":"/paper/2509.10324"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:55:54.167218Z","title":"Time series forecasting has moved beyond simply predicting weather or traffic, and is being used to learn patterns from any data that has an order to produce results","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.167218Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:25d5d6019e16c68a8af3344a3380b5a14d7687af518e00ee6f241d0a345b1492","observation_id":"71c3cde8-7291-4a29-b6b9-dd0887ff9782","resolution":{"observed_at":"2026-08-04T17:55:54.167218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.10324","snapshot_observed_at":"2026-08-04T17:55:54.171229Z","title":"The block diagram is arXiv:2509.10324v1 [cs.LG] 12 Sep 2025 shown in Fig 1","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.171229Z"},"links":{"cited_paper":"/paper/2509.10324","citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:31d64972b57bf9a2ff73dc2d956a72d72b85d88d29d18166b2ffb3bf0b5ae2ee","observation_id":"9b497da5-37e8-4bec-9e1e-f8ab71864b40","resolution":{"observed_at":"2026-08-04T17:55:54.171229Z","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":"2026-08-04T17:55:54.175096Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.175096Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:1ac43e59fab8c14422978924b24c230a9207d491c7bd8f805008773aa0fc4888","observation_id":"e9d9abce-1971-46c7-80e7-00bb0dbaea68","resolution":{"observed_at":"2026-08-04T17:55:54.175096Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-04T17:55:54.178749Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.178749Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:0d4c2299fceeb1bb4e042f80e506722d1841cc2204626b6ad15f32a839fa4c8d","observation_id":"19421a85-4a35-4fd5-b9a0-3f23fd4be40b","resolution":{"observed_at":"2026-08-04T17:55:54.178749Z","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":"2026-08-04T17:55:54.182112Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.182112Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:33dfe49664b76e812c7e892b891582c85a96fd1f70fef34f138ad2c7de6c8ef1","observation_id":"b038c9c4-ac47-4314-9071-20718b3024c4","resolution":{"observed_at":"2026-08-04T17:55:54.182112Z","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":"2026-08-04T17:55:54.185874Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.185874Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:331e871057c4ff9dd5429675d43574353971820244736b7390ca0fe1a723f1e4","observation_id":"7dbb02d2-b4da-4759-a236-119ed9178189","resolution":{"observed_at":"2026-08-04T17:55:54.185874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-04T17:55:54.189508Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.189508Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:baac17162080d9e5946d02d5c469544bc5d67787caa9e21b4c3819d357cd42fb","observation_id":"74f9726b-3535-4fe9-a9b9-4f45cf760bba","resolution":{"observed_at":"2026-08-04T17:55:54.189508Z","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":"2026-08-04T17:55:54.193390Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.193390Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:1781edd70c863414978d6e26e5ffdffd8ddf475c5bdbbbe60154c52358dab596","observation_id":"a1934c20-82e6-41ec-af17-e9fa7afa0fd7","resolution":{"observed_at":"2026-08-04T17:55:54.193390Z","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":"2026-08-04T17:55:54.196605Z","title":"In- former: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.196605Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:7569d6e159610b2b28874e09a66e68d7db4b15cab47a2843fbf4a1908d05bc3d","observation_id":"6b2040e4-d9f8-4568-bfe7-ac8453c04942","resolution":{"observed_at":"2026-08-04T17:55:54.196605Z","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":"2026-08-04T17:55:54.200133Z","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.200133Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:7507dd3a042fff48eb584265908ddad84d35cef24ee4468fd9728ace496c59cf","observation_id":"7a2d1f30-7cbb-44d0-965c-eab7957e67a6","resolution":{"observed_at":"2026-08-04T17:55:54.200133Z","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":"2026-08-04T17:55:54.203186Z","title":"Fedformer: Frequency en- hanced decomposed transformer for long-term series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.203186Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:52ba4c9af943af0907d2bbe4329ed3ab44a6e40ffd6d96f818e2d19331419dd2","observation_id":"c220522c-e8eb-433b-945c-d8f177e78154","resolution":{"observed_at":"2026-08-04T17:55:54.203186Z","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":"2026-08-04T17:55:54.206422Z","title":"Time-moe: Billion- scale time series foundation models with mixture of ex- perts,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.206422Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:244f0b58ff06a8ad928bd84a5f57dc97f4f09fe541bd96e942492b3944e9048e","observation_id":"54b98f02-cbbd-4160-b39f-7baccbb3fb82","resolution":{"observed_at":"2026-08-04T17:55:54.206422Z","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":"2026-08-04T17:55:54.209435Z","title":"Mixture of experts for time series foundation models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.209435Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:a40ef2999482a6f8b017bfb4b69caadb7854f47675f7fc277e85864d040a5644","observation_id":"a8bc6602-fde3-4bc7-bdc9-6ccc7f120e54","resolution":{"observed_at":"2026-08-04T17:55:54.209435Z","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":"2026-08-04T17:55:54.212607Z","title":"Are transformers effective for time series forecasting?,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.212607Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:f35414656752b2c61412910b419eec9570c2a4ffdc3a8313267d44061c3ab02a","observation_id":"8a889ba3-768b-486d-aa99-5367490fdf42","resolution":{"observed_at":"2026-08-04T17:55:54.212607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08248","last_updated":"2020-01-22T19:44:43Z","snapshot_observed_at":"2026-08-09T22:11:18.505621Z","submitted_at":"2020-01-22T19:44:43Z","title":"How Much Position Information Do Convolutional Neural Networks Encode?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08248","snapshot_observed_at":"2026-08-04T17:55:54.215713Z","title":"How much position information do convolutional neural net- works encode?,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.215713Z"},"links":{"cited_paper":"/paper/2001.08248","citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:e26711cfec4a86d81f7c654834af32129f36dae94a448c8e1c43196e901741fb","observation_id":"03be440d-a062-48af-acef-c1675854e7c9","resolution":{"observed_at":"2026-08-04T17:55:54.215713Z","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":"2026-08-04T17:55:54.219055Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.219055Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:d2ba9d88e6ecb0e0085c1c2658b504a7075a166cffc8d576b892672bb0acfb64","observation_id":"d8fc7fcd-8b65-41d2-b134-72321deba529","resolution":{"observed_at":"2026-08-04T17:55:54.219055Z","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":"2026-08-04T17:55:54.222028Z","title":"Modeling long-and short-term temporal patterns with deep neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.222028Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:631c3c2c758d7876df0c50293be7e2f7455dbe9de84cd74d68ca4eada7b749a2","observation_id":"5704db22-ff2e-483a-b0bd-01fbeb49635e","resolution":{"observed_at":"2026-08-04T17:55:54.222028Z","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":"2026-08-04T17:55:54.224930Z","title":"Reversible in- stance normalization for accurate time-series forecast- ing against distribution shift,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.224930Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:717455d858e2e3da312b4c4e9d4178cab1a75df2f22d1ba1a3a329b68e4b470a","observation_id":"b4adead4-904b-4d4b-8c6f-b3a39dfdd6aa","resolution":{"observed_at":"2026-08-04T17:55:54.224930Z","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":"2026-08-04T17:55:54.227868Z","title":"Adaptive nor- malization for non-stationary time series forecasting: A temporal slice perspective,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T17:55:54.227868Z"},"links":{"citing_paper":"/paper/2509.10324"},"observation_digest":"sha256:f921602e2cc77532f5cc4fbece61c7341f2edda5c8179080c0e8fc7b28a3ef9d","observation_id":"10e2004c-d2fb-4a51-b388-e17a12a39cd0","resolution":{"observed_at":"2026-08-04T17:55:54.227868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.10324","last_updated":"2025-09-12T15:03:49Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T19:19:16.586450Z","submitted_at":"2025-09-12T15:03:49Z","title":"ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":19},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2509.10324."}