{"as_of":"2026-08-14T11:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5b751ce56c76075e0cdefded665c88ba6fbe3a4fb735b17361b26523f79a31e4","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T00:53:56.699362Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2608.00513/citation-record","integrity":"/paper/2608.00513/integrity","json":"/paper/2608.00513/citation-record.json","paper":"/paper/2608.00513"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:10.083473Z","title":null,"venue":null,"work_id":"19f96872-6993-4682-b393-7ad30253842c","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.120973Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:08d8621daa5b4be314fd3cc250d22287961256d098acf07f7bed3ffb41fe69ca","observation_id":"a324d614-4460-4f79-ab68-13da52c0aa92","resolution":{"observed_at":"2026-08-05T00:54:10.204733Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:09.774769Z","title":null,"venue":null,"work_id":"38f9ca7e-2b0a-446c-a7f9-f8179cdac756","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.174955Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:2ddcad4432926482c2bc43bf55b10e7e6e6c8960078e2e943f9fb081af746e3b","observation_id":"dd242442-ba32-430e-a2ce-45217ee8221b","resolution":{"observed_at":"2026-08-05T00:54:09.922251Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:09.421673Z","title":null,"venue":null,"work_id":"c3708a12-ef78-4d67-9d19-911371269909","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.235545Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:531a72b0ff1373800b15752385b78bcb664d351ed8da8a010979f66b293279f0","observation_id":"330860ad-30c0-489a-8edc-2777cbf75acd","resolution":{"observed_at":"2026-08-05T00:54:09.584220Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:09.132060Z","title":null,"venue":null,"work_id":"1349f4aa-074d-4b2a-810d-f072a0c5f5f0","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.339875Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:f4dc0ee20b746643d4538c3ef1edf14b827da4943e52dd05988fd79803b09758","observation_id":"c57f9817-c10d-48bc-abfc-55598244a224","resolution":{"observed_at":"2026-08-05T00:54:09.260018Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:08.857368Z","title":null,"venue":null,"work_id":"219cae51-59b8-475e-b923-8baad206f848","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.389500Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:0a2420cc2baba020ca3629f5a8005b6668015f3389facc369e35f518da46febc","observation_id":"acd9b673-cacf-4002-ab5f-408f506dbfb3","resolution":{"observed_at":"2026-08-05T00:54:09.000749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:08.544169Z","title":"When an appliance remains in a steady state, the electricity consumption profile on the supply circuit remains relatively stable","venue":null,"work_id":"754e24ca-53ec-4d5c-871e-33fd3d816731","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.439749Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:fef81ae732e4d8abbce14e4d6bf8233b6340fcee05a83879d02ba4d82d0cfc56","observation_id":"03a7c430-0a0c-4b99-a850-a5bfc3d7d685","resolution":{"observed_at":"2026-08-05T00:54:08.681680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:08.199249Z","title":null,"venue":null,"work_id":"de37c84e-7034-4b02-bf97-6621e9f60a2a","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.530714Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:d92d688ba07ea8a15698232c3d4f9c3c2b4ca930138a7f8e2053b1ceed23ef9c","observation_id":"0647c6b0-b7eb-4585-8921-9c8090a66e74","resolution":{"observed_at":"2026-08-05T00:54:08.316659Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:07.833356Z","title":"The thresholds Δ and ε are defined by inequality (1)","venue":null,"work_id":"c506dea8-4c29-4448-9859-015b92956f2a","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.691372Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:074ced61b67fe4f807e235abfa0507ce8a7dbc1b82dabc09c8eae455950f24a2","observation_id":"1f1e92c5-80c5-4fb4-bb34-3109752dee59","resolution":{"observed_at":"2026-08-05T00:54:08.016074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:07.531183Z","title":null,"venue":null,"work_id":"3aade972-632c-4109-898b-cbabf4531795","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.760270Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:d98bc0087e18a9e8f84d7690054da3e934eca77d5d09b42915fb8f17d0f517a7","observation_id":"7296a823-392d-4297-b2e4-ef5eb016dac1","resolution":{"observed_at":"2026-08-05T00:54:07.682054Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:07.291619Z","title":null,"venue":null,"work_id":"58900676-f47f-4bce-bfcf-1be0efdc3eff","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.819512Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:1c36e0b680e1174db290f5056ed7bc08725e0c10eb8d56a898a118b5ac9a31b4","observation_id":"f6d5fbdd-9f13-42b5-8aba-0d8df29c0165","resolution":{"observed_at":"2026-08-05T00:54:07.399370Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:07.070137Z","title":null,"venue":null,"work_id":"884b2659-8233-4af5-ac7a-8da37180b5c6","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.875150Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:6de4c558bffe3dcf7883fcaebbe208e53937147bde9f694ce20d05d12b5f5556","observation_id":"174556b0-9e56-48b9-8990-a29eb4860738","resolution":{"observed_at":"2026-08-05T00:54:07.162232Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:06.832195Z","title":null,"venue":null,"work_id":"06de84cb-3625-4ca9-bc1e-39411d761d61","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.913201Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:5f6ce288d25f64ae9419676590ec7e62e1d0bba959cd70f4f5fdd6acd42a6e3e","observation_id":"e0d76831-27fd-44ab-a922-cc6f2e4a4431","resolution":{"observed_at":"2026-08-05T00:54:06.949580Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:01.122680Z","title":"A novel segmentation approach for work mode boundary detection in MFR pulse sequence[J]","venue":null,"work_id":"f0d2e955-45e8-4b9f-b2a7-398ac96c1016","year":2022},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.910998Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:dccbde94a3344b99ba58b580fbf8ec6767428c1cd0d6d548b5f871b8f354e01f","observation_id":"e63a9b7c-36db-40ef-865e-9ec86d940c98","resolution":{"observed_at":"2026-08-05T00:54:01.207385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:06.360604Z","title":null,"venue":null,"work_id":"ee393ca9-f833-43a6-b3f3-5029268ea8f2","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.022390Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:3498d640b1f7d9787c4993926aa718e6475cd06e6a0365cea6864c2311be7aab","observation_id":"dafaa72a-3709-4746-a2e5-b312ec46ec11","resolution":{"observed_at":"2026-08-05T00:54:06.476898Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:06.115074Z","title":null,"venue":null,"work_id":"e1bbc882-3eb5-4014-bbcb-24849feb8aa9","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.110686Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:f887cf717730e07ee720eecf05f3d9bd1165fd1cb1ddbccd6d8a2bb312111545","observation_id":"eef20231-18a5-4563-973f-1e3dada82049","resolution":{"observed_at":"2026-08-05T00:54:06.224565Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:05.837865Z","title":null,"venue":null,"work_id":"afa36951-4275-4ca3-84b2-de6c3020c3a7","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.166941Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:a9d8a61c2b16d96c8ad38c2f4d36f79dabb7c6a7fb8f9e1f435921048f74d6bc","observation_id":"5efd6f47-b639-45fc-817b-f0556aecb1d1","resolution":{"observed_at":"2026-08-05T00:54:05.951721Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:05.621318Z","title":null,"venue":null,"work_id":"63f229b1-7b97-4902-9576-db0387ae7221","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.316014Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:d384b2f691cbce306b9ea798c4bfbd4300f70e19d1c74530777990b9c10a2d1b","observation_id":"3d662c3c-b605-4d69-aa02-0b4433b1f882","resolution":{"observed_at":"2026-08-05T00:54:05.761274Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:05.379522Z","title":null,"venue":null,"work_id":"fa6be173-920d-4987-95b9-10706b7f9774","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.400146Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:48511ba4b080c816db4913bf7e8677c341475de82be90c0560710578edf16901","observation_id":"696bdb06-6838-40ff-8d46-969c43f2ba11","resolution":{"observed_at":"2026-08-05T00:54:05.492676Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:06.592361Z","title":"steady_segments are the output of Algorithm 2","venue":null,"work_id":"eae79af0-6d00-4ca2-abc7-ab6c28b63e75","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:52.964617Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:a2dc785abeca3e2c945491512508ec4b8b0cacde1c7bc24b48e61e0a694ae66f","observation_id":"3c2de50b-376f-4757-ad2c-7d250019ef8b","resolution":{"observed_at":"2026-08-05T00:54:06.715940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:05.088431Z","title":null,"venue":null,"work_id":"24a068f2-26c4-4458-9771-087650ba3717","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.554132Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:2ac414975f811ef79917d5a74310155660d6b0bfed94cdb7c0738b21fbf870a4","observation_id":"97b6f5fb-b166-429e-9d1e-af9ad0f992a2","resolution":{"observed_at":"2026-08-05T00:54:05.200692Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:04.845860Z","title":null,"venue":null,"work_id":"3d9c177b-e474-4e94-a582-e7ab1df59726","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.648139Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:f94f3f1330dc026305b4946e12bea95c4b488cec4e39da85762d983d1456b342","observation_id":"972a13f2-4c72-48e7-a2ff-cd11e99bc70a","resolution":{"observed_at":"2026-08-05T00:54:04.973535Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:04.585350Z","title":"Let 0 , ntt R ∈ with 0 0, 0ntt >> and 0 ntt <","venue":null,"work_id":"e46d50a2-69c2-4041-8c44-e6213687467b","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.714240Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:19cded78d7e21e2d2eafebc33062b80702958781f0cb28c114a75503986daf2b","observation_id":"41f1a2db-a93f-4f9d-a7ad-d1560dc614d9","resolution":{"observed_at":"2026-08-05T00:54:04.729155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:04.293620Z","title":"As shown in Figure 2, BayesSeg comprises a segmentation module, an evaluation module, and a parameter -optimization module","venue":null,"work_id":"9ccb721c-c4a9-47e1-a5dc-08e5afdf09c0","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.763705Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:237b11df6087632c6177e8c81c2dd0f46dc71fbadd05fdd11e5371e743fba2f8","observation_id":"2047b692-66cd-4f27-9d79-97ec2b859baa","resolution":{"observed_at":"2026-08-05T00:54:04.449223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5302.54353","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:57.449360Z","title":null,"venue":null,"work_id":"fbf269c3-fe6b-4df8-bd32-1590b77eb44e","year":1912},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.818303Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:c134a0ac4321c40295c6a22a0dc6a7a8bea0564f866ea997699d498ed13e89be","observation_id":"99205b04-b81f-495c-ae40-9b7876ce410c","resolution":{"observed_at":"2026-08-05T00:53:57.581048Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:04.017451Z","title":"making weight decisions for the user","venue":null,"work_id":"4040e2ea-2606-4fd1-833a-36f025b1f49e","year":2022},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:53.950415Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:6a9da1b9d47e764e5ce13f8d904919f9d90c8f12eb33f1f67e0faee45423cf9a","observation_id":"6f05ed84-36cf-4a39-a25e-0a4a1aa76e42","resolution":{"observed_at":"2026-08-05T00:54:04.195936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:03.766281Z","title":"A., ABID M","venue":null,"work_id":"4f1866f3-7c41-4b70-a77b-74ced5203cd9","year":2022},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.011712Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:38d466f73b1af4499ddaee9c19b7cfe7bd3d9722b2ea42388a1c9501acf37ed0","observation_id":"23a858c5-d3f9-4fb1-bdc5-548f958c3de9","resolution":{"observed_at":"2026-08-05T00:54:03.899786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:03.509711Z","title":"A Survey of the Research on Non-intrusive Load Monitoring and Disaggregation[J]","venue":null,"work_id":"f67a23de-d77a-467d-a48e-1b39eb1bde1d","year":2016},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.093558Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:33750c5abb07c61c1834cd41de97faa94796bd1a9ddebb003836e4ca4b0c0954","observation_id":"64b1eafe-5788-476f-8302-a922d611b756","resolution":{"observed_at":"2026-08-05T00:54:03.640919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:03.214675Z","title":"Non -intrusive load monitoring: A systematic review of methods, scenario- specific challenges, and pathways to practical deployment[J]","venue":null,"work_id":"0118b9af-1f81-492d-ae3e-929f05a99055","year":2026},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.165792Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:ba6ee142aacc76087c43f4bd1573935428902a678d5234cec04adf280c114224","observation_id":"101a1d7b-ea3a-490a-8575-a9e908c16208","resolution":{"observed_at":"2026-08-05T00:54:03.344342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:02.974144Z","title":"Research on Feature Model and Mining Method for Current Transition Sequence[J]","venue":null,"work_id":"647f7893-3fcf-4aac-bca0-5590ec3d38a2","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.256646Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:f5ba0c5b60c8f43b11e9e641af601e5811564b3ded305a544d13ae56d00f2c0c","observation_id":"ef991feb-c054-4f39-96d1-9e71f2c1f166","resolution":{"observed_at":"2026-08-05T00:54:03.089475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:02.694953Z","title":"A low -frequency residential NILM approach based on adaptive event detection[J]","venue":null,"work_id":"ff66fdff-332d-4ce7-94c7-e476500cb6fd","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.317190Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:9fcb743056cf8471d7796cbfbb66df81ded4ed1f28c1fed4319d4fd531029f33","observation_id":"c786f34c-3026-482f-bb70-df9f9a4bb1aa","resolution":{"observed_at":"2026-08-05T00:54:02.798815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:02.438172Z","title":"Unsupervised time series segmentation: A survey on recent advances[J]","venue":null,"work_id":"206ac386-6c83-49e0-b009-34a6db4b6d37","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.351663Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:44f828b119844cb7e6489c18e925b588e7a6d2f94a2db070e0c027fb6f9cfe03","observation_id":"83d5eab4-4894-430c-8df0-5fff55c41ac2","resolution":{"observed_at":"2026-08-05T00:54:02.627118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:02.296778Z","title":"Adaptive algorithms for change point detection in financial time series[J]","venue":null,"work_id":"a952e762-d535-421e-b904-72b502e11a72","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.408165Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:c6f14faba64119dfb6e3fef78319d58dff73643973504cdc17036e535ffdeb38","observation_id":"ddc5608a-77a2-42f3-ac9c-3cb100879c7d","resolution":{"observed_at":"2026-08-05T00:54:02.383770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:02.007649Z","title":null,"venue":null,"work_id":"b7559aec-b798-4400-8cb4-d804b1d9b0f0","year":2023},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.501519Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:ae0a9175cfea630a95aa5c6abcc52030c3fc71d7646d2e97b7392a912b7a4129","observation_id":"efff6abe-9966-4f78-add6-67db853a86c8","resolution":{"observed_at":"2026-08-05T00:54:02.173585Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.01783","last_updated":"2025-08-25T09:14:37Z","snapshot_observed_at":"2026-08-07T16:14:25.941125Z","submitted_at":"2025-04-02T14:46:42Z","title":"CLaP -- State Detection from Time Series","version":2},"cited_work":{"arxiv_id":"2504.01783","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.01783","snapshot_observed_at":"2026-08-05T00:53:57.089654Z","title":"CLaP -- State Detection from Time Series","venue":"cs.LG","work_id":"789e19ba-c01e-44aa-90f5-9d4056ebe9bb","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.560871Z"},"links":{"cited_paper":"/paper/2504.01783","citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:cbe65a1faadf2479c44abff0a5762f8274769ac13cef1b2e217ce48a79f495bf","observation_id":"34464876-255c-43ff-aced-347fbb3fed5f","resolution":{"observed_at":"2026-08-05T00:53:57.225007Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:01.707675Z","title":null,"venue":null,"work_id":"65ea4eb1-f409-41ca-b05d-27da0e0e9a71","year":2019},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.673422Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:3c8e38b4c9d351abdced6bb385e381c3a40e2ae90153630b8bc27f75eb52a693","observation_id":"4a249aee-48e4-4caf-bd44-0fa411b69b37","resolution":{"observed_at":"2026-08-05T00:54:01.781493Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:01.496332Z","title":null,"venue":null,"work_id":"ba96eb28-83ad-4e0f-a8fa-2dd6c033f0a3","year":2016},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.730123Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:f4552dda03a9ee15f2fd206f586773cc3d9e2cdb027db0c30eba715a88653f0a","observation_id":"0e6ed634-70c0-4988-bae7-0db3cf672c60","resolution":{"observed_at":"2026-08-05T00:54:01.622833Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:01.286125Z","title":"Time2State: An unsupervised framework for inferring the latent states in time series data[J]","venue":null,"work_id":"64235383-da78-41a5-a6f0-07e0721d28ab","year":2023},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.855172Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:1d65848d95a0a6a1bc28da5dfba170b7e32f718d48dea35577b2d01f898af061","observation_id":"0adec100-8dd5-4ca7-aad0-1c160cb619c3","resolution":{"observed_at":"2026-08-05T00:54:01.404755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:00.837628Z","title":"Analyzing the performance of biomedical time-series segmentation with electrophysiology data[J]","venue":null,"work_id":"c1781b2e-457c-4766-9e4f-9e294e5354e7","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.949485Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:321b2c5a3586649ffca5dc0a8f3b8eaca7b5405a3bab16319437f5d7066b5244","observation_id":"0e9c24cf-f3c8-4457-9c51-13fa91e473f6","resolution":{"observed_at":"2026-08-05T00:54:01.053838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:00.626263Z","title":"A residential labeled dataset for smart meter data analy tics[J]","venue":null,"work_id":"19bbe455-ad6b-437a-b784-aae3478aed80","year":2022},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.008415Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:89507d918f0f987b051c8b200b5ca250ee4e202e743fb24c701f9f47fb677696","observation_id":"0db23c07-9762-4846-a2cb-3ff0ab5d1bad","resolution":{"observed_at":"2026-08-05T00:54:00.748088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:00.368403Z","title":"Transient event detection algorithm for non-intrusive load monitoring[J]","venue":null,"work_id":"704ec1bb-aa45-4546-bd55-b2a7c12beedf","year":2011},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.049002Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:a5fcd0018d55c4f7e9e5e0fb644009afbfbb001b0a1fcb9a0836ac69c37813fb","observation_id":"13553774-8ad4-4b79-af3c-48c4b8d6ffb6","resolution":{"observed_at":"2026-08-05T00:54:00.497572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:00.096206Z","title":"Nonintrusive load monitoring (NILM) using a deep learning model with a transformer-based attention mechanism and temporal pooling[J]","venue":null,"work_id":"1bfc821e-a533-4a65-9ddb-4e75788813ea","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.105940Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:fab992d9d30cee526962ef97a4c82453b64f575ce1b1438668858d6e04e5eba0","observation_id":"22262c26-6465-4e1a-8690-b687a63d8ac0","resolution":{"observed_at":"2026-08-05T00:54:00.229975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:59.803767Z","title":"Enhancing non-intrusive load monitoring through transfer learning with transformer models[J]","venue":null,"work_id":"f261c072-2488-47a2-aee6-8d901c34d933","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.244839Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:0584f1b8445c880e3ec75415a4d8bdcf9ab7c59f77aef4899eb8f6373a17c57e","observation_id":"3e174615-2c37-4631-bffe-896c82bb1205","resolution":{"observed_at":"2026-08-05T00:53:59.939598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:59.552351Z","title":"Non-intrusive load monitoring model based on SimCLR and visualized color V-I trajectories[J]","venue":null,"work_id":"d4f1d9e8-3294-4b44-9284-de6ec9da3b97","year":2026},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.378108Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:fc4e7b677782ade24277d97195a86000fda26ed01e0fc6ac1f256855f5bdcb97","observation_id":"56ce09fa-7db9-4bab-adbf-77f2f6eefc34","resolution":{"observed_at":"2026-08-05T00:53:59.678932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:59.328363Z","title":"N., et a l","venue":null,"work_id":"a829a614-9bfd-4f0b-9151-1aee77613154","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.508348Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:057a8989c24ca103c8a2c20477a588e4375aa89bb4a919908c55383b5df633eb","observation_id":"eff08559-3189-471e-bf9d-4593d8257a8f","resolution":{"observed_at":"2026-08-05T00:53:59.416981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:59.165673Z","title":"Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization[J]","venue":null,"work_id":"b975f681-d038-44f2-86e4-9057a22c566d","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.646520Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:e5f3e4f70007e047969f358c3cbf01ca46ee9b543aeed2fa965d9e8f92684eef","observation_id":"771ef5a6-b190-4d23-bd5c-922f9f667492","resolution":{"observed_at":"2026-08-05T00:53:59.258538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:59.029267Z","title":"Change -point detection with deep learning: A review[J]","venue":null,"work_id":"7c6f96d8-984e-4fbc-b427-b2251657e05d","year":2025},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.796933Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:757a42ddd44c32928d69cdf5ab13b4172be6453304f53e9aaec9833c4b4b02f6","observation_id":"0b1122a9-5f94-43b9-aaea-84f3e77213bf","resolution":{"observed_at":"2026-08-05T00:53:59.099901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:58.817950Z","title":"Automatic change -point detection in time series via deep learning[J]","venue":null,"work_id":"693e4e52-7daf-43f9-a6c4-d252927d2688","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.901617Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:9f3004d0b67567c6168c98c4219110e49292f4d222f93136ce596fb0fd3c9218","observation_id":"d9ebd69a-292d-4c87-8d13-50fdfbf7976d","resolution":{"observed_at":"2026-08-05T00:53:58.919445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:58.592986Z","title":"Online neural ne tworks for change-point detection[J]","venue":null,"work_id":"06d16736-3965-4dc0-946a-9e67e93e6737","year":2026},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:55.961532Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:0d298bec2e14963e3df1a32eb22acf926ac3f6f9c18c739d1867735955633dd4","observation_id":"03fa9fbe-bdd1-42c5-89a7-f8ded1bc48fc","resolution":{"observed_at":"2026-08-05T00:53:58.705931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:58.381646Z","title":"Short-term power load forecasting based on Seq2Seq model integrating Bayesian optimization, temporal convolutional network and attention[J]","venue":null,"work_id":"1118f3c6-c2e3-4817-9e84-1ad56f06b0e5","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.093198Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:bc4a2448bb79748e6752484001d50bd0688b2e5c5a83d38bdbf465597bbd6e28","observation_id":"eea281fb-50e2-49f4-a2a8-6448d37333a1","resolution":{"observed_at":"2026-08-05T00:53:58.483663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6212.2022","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:56.885978Z","title":"A hybrid neural network based on Bayesian optimization for non-intrusive load disaggregation[C]","venue":null,"work_id":"5d1de8b3-2d71-4407-b23a-0752ece02220","year":2022},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.191169Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:aa02f23a2fc8bbf557f79d0f3d5b6ced7713e2a86a8d5c53548b8ca5784cb761","observation_id":"cbbcdea0-4921-416b-bd37-8f88ca99a09f","resolution":{"observed_at":"2026-08-05T00:53:56.988538Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:58.227622Z","title":"R., KHALID S., et al","venue":null,"work_id":"c8060357-58ec-4041-8cda-3d3fa7f79c0c","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.341242Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:5d80431a4b54b93567c592b358d4c144a2e4305f13a065e74cd9cb11a4bd9418","observation_id":"aa7e05cc-9425-4088-983d-bb21d6e421bf","resolution":{"observed_at":"2026-08-05T00:53:58.283223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:58.073864Z","title":"Evaluation metrics and statistical tests for machine learning[J]","venue":null,"work_id":"c2f98629-bc1d-4a8e-9dfd-fd7eca56d6e8","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.489628Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:dd5b3dda630bf6ca45751ca1f0a128f16f8fee221e23c4a3ec509efc4b4ee77d","observation_id":"83b965b4-b5a2-4831-a636-4c5ab44401a6","resolution":{"observed_at":"2026-08-05T00:53:58.160582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:57.839596Z","title":"A closer look at classification evaluation metrics and a critical reflection of common evaluation practice[J]","venue":null,"work_id":"f43fe21b-fa23-4b5b-a1f5-f4d1e1305811","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.582956Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:0c0ed3351caa6497a87a754b477ce310a4cfa49960bcc6a9e008800d94b0d715","observation_id":"3a76b229-7824-4bb6-a249-4a9df2a799f3","resolution":{"observed_at":"2026-08-05T00:53:57.992275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:53:57.684747Z","title":"An experimental evaluation of anomaly detection in time series[J]","venue":null,"work_id":"b79a12f9-c6d7-493d-8397-2d01d11bc406","year":2024},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:56.699362Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:dc9e8624779be78e4ffd2bc2501d377838ca7f09f7437c37af2cdca0197ab17c","observation_id":"4d8ada95-e42c-4f07-a00a-82d2ebe3209c","resolution":{"observed_at":"2026-08-05T00:53:57.747874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T00:54:01.856201Z","title":null,"venue":null,"work_id":"5c3aa424-ef6f-476b-b5bc-52af86dec505","year":null},"citing_paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T00:53:54.599607Z"},"links":{"citing_paper":"/paper/2608.00513"},"observation_digest":"sha256:0fb3f9a47d9f867a448d092a984d095dffe29ee5f5a9959be937a661ec23d79c","observation_id":"a863a53c-59c3-4343-bb6d-aa8ca3b18cad","resolution":{"observed_at":"2026-08-05T00:54:01.916409Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.00513","last_updated":"2026-08-01T08:22:49Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-14T01:29:18.994877Z","submitted_at":"2026-08-01T08:22:49Z","title":"BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":20,"verified_exact":3,"verified_fuzzy":31},"total_outbound_references":55},"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 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.00513."}