{"as_of":"2026-08-12T13:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7388f31b6cfb8719f82d89eddb60d3cc7adaa9b707261973dd2204f74dd4ee8","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-07T14:29:59.634017Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2604.26081/citation-record","integrity":"/paper/2604.26081/integrity","json":"/paper/2604.26081/citation-record.json","paper":"/paper/2604.26081"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Internet traffic matrices: A primer","venue":null,"work_id":"7b7b1b7c-3d57-420c-8dcb-020713255c53","year":2013},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:a817ec3afc48bbba65ec04803e987dc07ba45e27c6e99b5d83fa45d63fd765b8","observation_id":"1fef169a-c7ca-4c77-8f40-3e4593778560","resolution":{"observed_at":"2026-05-27T04:23:32.395045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Traffic matrices: Balancing measurements, inference and modeling","venue":null,"work_id":"679815f1-b499-40d4-884c-f5e2471bb2fd","year":2005},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:6a703c021c58b33e1102d3e404f647e6faecdefb9b930e9998d19170b3596b43","observation_id":"62c3c8fb-75e6-4949-a81b-80ca5f56670f","resolution":{"observed_at":"2026-05-27T04:23:32.376112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neutm: A neural network- based framework for traffic matrix prediction in sdn","venue":null,"work_id":"9215ae19-928f-49d3-af3b-2992ff8111d8","year":2018},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:c93042306f1ef30e74fad6a349572c29ff3d4dd44cb80f16d606a96298e4e416","observation_id":"00690592-1bc6-416c-8da5-6d5a9d6a4e45","resolution":{"observed_at":"2026-05-27T04:23:32.431882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learn- ing based network traffic matrix prediction","venue":null,"work_id":"3cc2aaa9-f53c-4110-adf0-a858d81679c8","year":2021},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:625a7cc9fcf47f536d4cbec64cd092e9838b7b976ce6c11d3304537b395f6bd3","observation_id":"306c32e6-74c2-4e81-afa8-cb0b8ee04f78","resolution":{"observed_at":"2026-05-27T04:23:32.442241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Internet traffic prediction with deep neural networks","venue":null,"work_id":"45f04dd0-7b5b-43d7-b379-336989810daa","year":2022},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:df2e102e01ffda1cc7f34e4ebf91325665fdc3677f08c6619750a1b980809e64","observation_id":"9249f267-0d80-4086-8f57-84cf152c0062","resolution":{"observed_at":"2026-05-27T04:23:32.439208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.00444","last_updated":"2021-03-26T23:34:48Z","snapshot_observed_at":"2026-08-05T15:39:04.381662Z","submitted_at":"2020-08-02T10:22:05Z","title":"Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality","version":3},"cited_work":{"arxiv_id":"2008.00444","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.00444","snapshot_observed_at":"2026-07-04T14:39:57.050054Z","title":"Principles and algorithms for forecasting groups of time series: Lo- cality and globality","venue":null,"work_id":"b7b87dd2-1e27-40c6-a286-e739a5e4ab47","year":2008},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"cited_paper":"/paper/2008.00444","citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:90b61f18f6d64c5bd95ee1708ca269d726572e1d0f3d3fcb727d9042f1fae62f","observation_id":"5b33de5c-0edc-4612-80f3-a24bd5f83dcc","resolution":{"observed_at":"2026-05-12T00:41:26.224673Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2008","venue":null,"work_id":"ed3ce7bd-0749-42de-ad60-6c0456644e74","year":2008},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:b1ac47f5fa60f513ff74e1cadb0274a564410317c62eb4d751ef92aa59c24696","observation_id":"bb7f5033-6bcb-4b9a-a7c3-23d3dab4edd3","resolution":{"observed_at":"2026-05-27T04:23:32.386200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.03222","last_updated":"2018-09-12T08:03:34Z","snapshot_observed_at":"2026-08-11T11:52:49.484142Z","submitted_at":"2017-10-09T04:08:15Z","title":"Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach","version":2},"cited_work":{"arxiv_id":"1710.03222","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.03222","snapshot_observed_at":"2026-07-04T23:02:59.103979Z","title":"Forecast- ing across time series databases using long short-term memory networks on groups of similar series","venue":null,"work_id":"7b53f1fa-4e2a-429e-8fb2-c9e0ef5c1304","year":2017},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"cited_paper":"/paper/1710.03222","citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:37ef7f3a3d93f91f336633d5ddd2ee7e32860eb890386e8f3e3f38771e5a6317","observation_id":"651883d3-35da-4e77-b0db-2d553a1ddfc5","resolution":{"observed_at":"2026-07-04T23:02:59.103979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, Abilene network traffic matrices , https://www","venue":null,"work_id":"8bd41672-86c6-4096-a648-b7aa02ea9c46","year":2025},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:29b4a89aed943371da8f0f5bf785400dd88aae156d5e2c5d76c3a6fc0f79652b","observation_id":"a3e0b0cf-9e1b-462e-a284-7aa8964dfcec","resolution":{"observed_at":"2026-05-27T04:23:32.421517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Incorporating intra-flow dependencies and inter-flow correlations for traffic matrix prediction","venue":null,"work_id":"dd15c8c3-4128-49db-9156-941fbf06712e","year":2020},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:364bbb047aa6ae395bfb56c7456b68e13bd283162b4f00059920ddcf29b46bde","observation_id":"b60ebbc3-e8ec-40a1-86cf-2d8a4344421f","resolution":{"observed_at":"2026-05-27T04:23:32.338389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/itl2.322","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Internet traffic matrix prediction with con- volutional lstm neural network","venue":"Internet Technology Letters","work_id":"a1fb7def-4bd5-407f-bf3e-629f0ac4c115","year":2022},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:a0545722a18dff7ae024ec5fc75be8c9e71560f1c4975a5f3c8695feb654199b","observation_id":"0883d626-f9ba-4227-b878-b69a53829bdb","resolution":{"observed_at":"2026-05-09T02:49:43.530714Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.15072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving internet traffic matrix prediction via time series clustering","venue":null,"work_id":"98857107-2f58-4e23-8ae6-fcb7028737c7","year":2025},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:73a16a06ba2508675e1c9937aa93d17caa7e7be2821aee7d0d2249142466eee5","observation_id":"dd73657c-e6b5-49db-8876-ee26dc6f7621","resolution":{"observed_at":"2026-05-12T00:41:26.228477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2509","venue":null,"work_id":"e155c5b7-51bc-4e60-bbe4-e4dcef219d4c","year":null},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:03907a9c8b36844c2137fb92e5bc9ac4c1bdc31b16f571f71bd66e1072bab20d","observation_id":"92278246-87ca-497d-81a3-e4ebee6d75ac","resolution":{"observed_at":"2026-05-27T04:23:32.418822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Traffic prediction for dy- namic traffic engineering","venue":null,"work_id":"3d4b0c8f-9c91-4a19-bc92-0bc92144e70d","year":2015},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:8b4912b6bf973e44b087435d0d3848a71794279f5cb71ce4dced3b797e5323cb","observation_id":"75974dbf-742d-49d3-b8f8-f8f40b86e9c4","resolution":{"observed_at":"2026-05-27T04:23:32.407088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to route","venue":null,"work_id":"5a867ea9-a63a-449b-860c-7cd67e3ab477","year":2017},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:2cc9bc2287f387e861d085e27361a0ea1fb341690db2b476993dc52e9cdcdca1","observation_id":"50ea16f0-8457-46bc-bafe-f9c49f2c2d0d","resolution":{"observed_at":"2026-05-27T04:23:32.435202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards traffic matrix prediction with lstm recurrent neural networks","venue":null,"work_id":"47972e2e-69ce-4534-81f6-e6549dccc962","year":2018},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:eadece91104d07c3a5eb5df31e5259eb878645eb5fdf4f2963c5204ac47ef512","observation_id":"b0a46bfd-ec86-474d-81b3-af735a20b3a2","resolution":{"observed_at":"2026-05-27T04:23:32.355461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learning-based traffic prediction for net- work optimization","venue":null,"work_id":"31a17ca6-4683-4f8c-82e9-0adbd2c3d7d1","year":2018},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:2c126531e97a6c9214398764f1959d3397b57be69547e6985ef66cbda47f4b2b","observation_id":"90b76072-2077-497c-8bfd-e86779d439af","resolution":{"observed_at":"2026-05-27T04:23:32.346956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Network traffic predic- tion using recurrent neural networks","venue":null,"work_id":"4a06660d-da15-4c07-a4b2-324d15004e5a","year":2018},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:ee75403f5d9b2118e4358be5ae0a5a3bce7b60457f47c9feafa2736c49b30e10","observation_id":"72ce7778-092a-475d-8f82-2115ef07b24d","resolution":{"observed_at":"2026-05-27T04:23:32.352561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An ai-based traffic matrix prediction solution for software- defined network","venue":null,"work_id":"bf0df016-79ce-4e5a-a450-371fe08f7659","year":2021},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:dece13fcf1abbf53f20b0ed74d542cef2d7b08bd5af634a952dfbaf60cf65007","observation_id":"b78858c6-ea43-42a1-a29f-90850d4e730e","resolution":{"observed_at":"2026-05-27T04:23:32.401489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Flow- by-flow traffic matrix prediction methods: Achieving accurate, adaptable, low cost results","venue":null,"work_id":"9b1411d2-6718-4fb3-963e-eb9d0884e582","year":2022},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:65c2ee512e9cbf23c0746e5fe04d58a1915ae06e666473c8858548e9890e1fd1","observation_id":"c9294517-bde3-4bd9-8aca-2355b45ed69e","resolution":{"observed_at":"2026-05-27T04:23:32.344119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/1062082","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Network Traffic Prediction Using PSO-LightGBM- TM","venue":null,"work_id":"155048de-343f-49ea-b4ef-51a8b706986a","year":2024},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:eb1dd6029fa51ff3301b359590fddcb22377947a63df6af992129861a3e32435","observation_id":"82900132-f2ff-4770-b2ac-6b99bd7930a2","resolution":{"observed_at":"2026-05-12T00:41:26.231888Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pre- diction and correction of traffic matrix in an ip backbone network","venue":null,"work_id":"c768ffc9-27ed-4586-ae9f-fb75d05d4581","year":2014},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:97733d59c4664724d034daeef94fb4852a7f6b7284c65fd7932847d7f5b0e2da","observation_id":"6438aaed-59db-46be-abe2-e3d46c9631e7","resolution":{"observed_at":"2026-05-27T04:23:32.380226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Traffic matrix prediction based on deep learning for dynamic traffic engineering","venue":null,"work_id":"9b3add52-9037-40d1-8ad6-a41012b2b3e7","year":2019},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:6d4d7c0f7e5c4ab006cf021515b452e62a28fbd55ef8cb33c8fe1713503aade3","observation_id":"b316ea04-e97c-46e5-bf60-9f1609553cf5","resolution":{"observed_at":"2026-05-27T04:23:32.383303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Time-series clustering - a decade review","venue":null,"work_id":"a71cb8fc-7eef-498e-88fd-dc58e45a58ee","year":2015},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:9481b56cd54033d02edceb138e85b0ca5290f70643990a2608cc30b86732a8b2","observation_id":"03bb51cb-a0cc-40da-9573-d3cfc7d5be81","resolution":{"observed_at":"2026-05-27T04:23:32.335435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.is.2015.04.007","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Time-series clustering – A decade review , journal =","venue":"Information Systems","work_id":"acec79bb-abd8-4648-8361-13480f956e69","year":2015},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:780dadcab918d5d70e316f07bd11f61560d85a51c24f18a6dac573f7ee3c08ac","observation_id":"92261ed1-0798-4fd8-ba05-f149fb6d0fed","resolution":{"observed_at":"2026-05-09T02:49:43.532540Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/icdmw","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T18:02:26.813442Z","title":"Shapley values of reconstruction errors of pca for explaining anomaly detection","venue":null,"work_id":"d46daa6a-a626-48fa-a259-32614add978b","year":2019},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:5b8425a631fc7ee044f4e9d3fc657e197b7c1c0f3873cc045061258477c2ec0a","observation_id":"b3c8f85f-63a5-4a86-9803-fb483af8234d","resolution":{"observed_at":"2026-05-09T02:49:43.529058Z","resolver_source":"doi","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Characteristic- based clustering for time series data","venue":null,"work_id":"a1756f7c-45f4-4a16-97c2-fdfbceeb8515","year":2006},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:f42b5ee355aebdebb6986e4725fcb9e990aecd7201387662ddcef6d4e12b3ab1","observation_id":"c6abd01e-17be-440d-9f10-c7bdd21e9c31","resolution":{"observed_at":"2026-05-27T04:23:32.389145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20582","last_updated":"2024-12-29T21:04:35Z","snapshot_observed_at":"2026-08-10T23:14:55.733471Z","submitted_at":"2024-12-29T21:04:35Z","title":"Bridging the Gap: A Decade Review of Time-Series Clustering Methods","version":1},"cited_work":{"arxiv_id":"2412.20582","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.20582","snapshot_observed_at":"2026-07-09T16:06:20.094068Z","title":"Bridging the gap: A decade review of time-series clustering methods","venue":"cs.LG","work_id":"2fc3a765-948a-4b19-9b13-2db72b3822cd","year":2024},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"cited_paper":"/paper/2412.20582","citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:9f371deaac4bb417fe11ce573ba550cf4a8e113532da10eb14a8f462c71bca0a","observation_id":"9f6547aa-5953-45fe-a58f-12c870d4a96b","resolution":{"observed_at":"2026-05-12T00:41:26.242499Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Forecasting histogram time se- ries with k-nearest neighbours methods","venue":null,"work_id":"1dc74d62-999d-4831-b10b-249dd5b53f94","year":2009},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:b8479a0f8ecf5be7dda2c9549cd818f0de33f91310023c8f68a0b6d4c8eef8eb","observation_id":"7ffe80f1-c30d-41b5-8cf0-bc7eda18f4ef","resolution":{"observed_at":"2026-05-27T04:23:32.398092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Histogram-based cluster- ing of multiple data streams","venue":null,"work_id":"6109082b-d3e6-4d12-8505-994d8ae95629","year":2020},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:225259ffa4f5417c4fc7517d18be9726d5a4c67aceda76a4ba27aeded00f6f2a","observation_id":"9126bb12-71cf-4545-a8d6-c70c91ded2bd","resolution":{"observed_at":"2026-05-27T04:23:32.424735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach","venue":null,"work_id":"0f3f869d-e303-4e6a-9678-519a9e1d288a","year":2020},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:e832961204f52260d4ee7fe690eacab0d51e31e276e1e4bff8c8bef27e36329b","observation_id":"bf8b1bdb-af98-4bce-a76c-a22442e74712","resolution":{"observed_at":"2026-05-27T04:23:32.428407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Feature-based classification of time-series data","venue":null,"work_id":"a228083a-1b4d-44f0-8c33-671eed8538bc","year":2001},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:8eeafc34baecdcd1162c6118b7c6f03f44dbfe84fc923c4cf2dbb3fbd54b7d17","observation_id":"d43dfe67-e3fb-48f4-a1e4-258a21971efb","resolution":{"observed_at":"2026-05-27T04:23:32.358704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"K-shape: Efficient and accurate clustering of time series","venue":null,"work_id":"272873f6-908c-4fb3-bd54-9488808c7803","year":2015},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:09b60ac026a0fb02ff10ed999fbffedc2f6246c13100002eff4e5076458138d6","observation_id":"fb793553-537a-48cd-913b-a3c1c54bbe85","resolution":{"observed_at":"2026-05-27T04:23:32.416135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A hierarchical feature-based time series clustering approach for data-driven capacity planning of cellular networks","venue":null,"work_id":"d5cd9e07-c8af-44bd-9f01-5c7327266171","year":2025},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:fd5fd0046fb09985c84982ecffaefd304274b66b015444b8cd561e81de250ae2","observation_id":"eb362f79-08c6-4171-93fd-dd2d1c1cbec3","resolution":{"observed_at":"2026-05-27T04:23:32.373504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7291.2020","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Application of agglomerative hi- erarchical clustering for clustering of time series data","venue":null,"work_id":"62303c74-a729-4d71-9d70-adb0d041857a","year":2020},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:20a4f55607b24fd78175a866186d5cf52e1c7c48460b0da4641b9d4b28c282c0","observation_id":"d157ddaf-73c0-4a48-b8c1-3cfa64364cf9","resolution":{"observed_at":"2026-05-12T00:41:26.250553Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A study of hierar- chical clustering algorithms","venue":null,"work_id":"1db8496d-04e2-4e38-a842-344cee98ca31","year":2015},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:b844bd2543a1e90ccff9852ba6759d947738919842f422bb28a28918d78b8308","observation_id":"eb8e1ecd-e5a2-4100-a37d-8627b92813b7","resolution":{"observed_at":"2026-05-27T04:23:32.413299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3077136.3080684","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The impact of linkage meth- ods in hierarchical clustering for active learning to rank","venue":null,"work_id":"4e26dfbb-7c69-4659-a90c-539852244502","year":2017},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:74e42e395e6bdfa136eefe9504a3b5cfe31b26aa1a162c10e98742437eea3379","observation_id":"6f5f7820-6cfe-4ad5-9457-5944c3a8026a","resolution":{"observed_at":"2026-05-09T02:49:43.527267Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Time series clustering using fragmented autocorrelations","venue":null,"work_id":"3519733b-2e1d-49b3-8232-8893857c8142","year":2024},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:bab9acc5655bc47397d3d0805606436c9de278dcdc76751e035c335b674859e5","observation_id":"67c2df9e-7a16-4701-b829-482a00a34c33","resolution":{"observed_at":"2026-05-27T04:23:32.445997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust clustering for time series using spectral densities and functional data analysis","venue":null,"work_id":"7e7bbcf7-3c29-4f03-a242-da110a80269c","year":2017},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:2e534df25aab26267ce2f43c374a542273ba9f6c6fea8c3aead909abfa1e28a7","observation_id":"5837c7ee-be16-4b62-9aeb-ba3e3f444dc6","resolution":{"observed_at":"2026-05-27T04:23:32.410174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The jensen-shannon divergence","venue":null,"work_id":"2a7f637b-d4f4-4665-8d9e-b15ef892448c","year":1997},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:8b41ffadf06b8e6e409f108be7d72aa079d0487004205d37f8b1f42d4f987afe","observation_id":"b36e602b-a97a-4863-8c49-96a4f3875df9","resolution":{"observed_at":"2026-05-27T04:23:32.404327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Performance guarantees for hierarchical clustering","venue":null,"work_id":"03b2c09b-5779-4f7a-b8a3-5ba92ff67191","year":2005},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:d428211262d1f37dbd1c7fc1f4ec583d9e174370e9e8a3e8a6295da88eee85e3","observation_id":"36367b2f-2fdd-4e97-a593-c7c98a110e74","resolution":{"observed_at":"2026-05-27T04:23:32.365132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms","venue":null,"work_id":"39635c3d-abb3-4164-89f5-42199bfa2995","year":1967},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:1a62ee921215ec876647bc55e4a78b52a421e34c899b1cefc3b6fab133045686","observation_id":"d08e8781-bc47-43be-9883-29728b1d4ba0","resolution":{"observed_at":"2026-05-27T04:23:32.368013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Providing public intradomain traffic matrices to the research community","venue":null,"work_id":"38eb4156-85df-41ab-bb4d-0ed58c44043d","year":2006},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:90ed87d6571acb2590fc356daa5fc8f22c13245eff0403da6dd39ed3d251529c","observation_id":"08de0567-2f76-407b-89b0-05754705ba06","resolution":{"observed_at":"2026-05-27T04:23:32.371001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Prophet: Traffic engineering-centric traffic matrix prediction","venue":null,"work_id":"0e88f0f4-eac2-4c85-afb4-bdc811abbce8","year":2023},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:3c8a65e4566255e80e8373e62457cbcb663f78d0e9ef99dfed0226622b25ad5e","observation_id":"414172cd-a3e8-417e-9530-f4db46c2808d","resolution":{"observed_at":"2026-05-27T04:23:32.341112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Comparing partitions","venue":null,"work_id":"fb92333b-304e-451b-9930-9b8d7f8c4257","year":1985},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:5f72bd14756485c820fa579d49cb3d0abec84bc8bc021fcf5130fa9f17e3e5ea","observation_id":"1425d260-a5b5-479e-bbf1-cec18fd3d296","resolution":{"observed_at":"2026-05-27T04:23:32.392061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cluster ensembles—a knowl- edge reuse framework for combining multiple parti- tions","venue":null,"work_id":"d8f2dd29-d8aa-4e2e-885d-919bf93e5d51","year":2002},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:eea87a9b0409a27ae89f3ecf2792098820f8b34a97dc6b5b881db982e04a789e","observation_id":"5d3417e3-8802-4deb-ac7a-8b6061323319","resolution":{"observed_at":"2026-05-27T04:23:32.349665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:f415e0db6dbf653a27f87cc8fb9d186f74902b98242de310a03ee1e0ab79a3fe","observation_id":"56a8bff8-b43e-44d9-bf5f-e179ab6e2a66","resolution":{"observed_at":"2026-05-12T00:41:26.236020Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Finding a ´kneedle","venue":null,"work_id":"b7fe2d03-36ca-4d60-bd9b-25bc156aaf07","year":2011},"citing_paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-07T14:29:59.634017Z"},"links":{"citing_paper":"/paper/2604.26081"},"observation_digest":"sha256:b0b76f6a309dcce8b1e8a7427730d384852736cdb046fb9a80c7ff8d398aa74c","observation_id":"45daa6ff-1da6-479c-b91e-422c1108ec3b","resolution":{"observed_at":"2026-05-27T04:23:32.361848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.26081","last_updated":"2026-04-28T19:46:55Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-02T14:37:21.669440Z","submitted_at":"2026-04-28T19:46:55Z","title":"On the Role of Time Series Clustering in Traffic Matrix Prediction"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":10,"verified_fuzzy":37},"total_outbound_references":48},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2604.26081."}