{"as_of":"2026-08-10T21:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f798dff5c103adc7a2c99a5a7f64fd14bffffa435c6e416ee85355405f2066bd","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:09:18.428363Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2507.11729/citation-record","integrity":"/paper/2507.11729/integrity","json":"/paper/2507.11729/citation-record.json","paper":"/paper/2507.11729"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:09:30.011773Z","title":"Madeira, and Alexandre P","venue":null,"work_id":"7ad09723-b6f2-4644-9cbf-76a236042674","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.449383Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:6556ca353c21993dea34787f0e60f329f789fde4ce850aaa82915cfe9a78b749","observation_id":"c5b0e8e2-82e1-4149-8cb8-01cb18e7450f","resolution":{"observed_at":"2026-08-06T17:09:30.117597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:29.767162Z","title":"”A scalable ensemble approach to forecast the electricity consumption of households.” IEEE Transactions on Smart Grid 14, no","venue":null,"work_id":"e5bbdf4e-3102-4879-ab19-e9fcd3180911","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.502022Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:d094d4552e7e6720405fc97fd8d613ca79b7b25c78a220d5dce44cb19b87a173","observation_id":"50b31b3d-620a-46b9-b639-a0e09c8ef479","resolution":{"observed_at":"2026-08-06T17:09:29.883839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:29.508567Z","title":"”Global models for time series forecasting: A simulation study.” Pat- tern Recognition 124 (2022): 108441","venue":null,"work_id":"af2b65b7-2d21-4c08-aa42-0583a0fd02c3","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.563368Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:b26925452a6ee2adbf55fed804822d7121440c5deafee8342543a8c3fe92e614","observation_id":"9d982fb6-f0e4-4ed0-9d46-cb705c1d7b0a","resolution":{"observed_at":"2026-08-06T17:09:29.630192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:29.310196Z","title":null,"venue":null,"work_id":"5e930e04-a4eb-4f8d-9a04-c4f271b6919c","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.640430Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:fa2900858dbbbbf7b7b0fa100a83451ea6954538b9408c07cf0d8f98cbc5fa24","observation_id":"08247c4b-1b02-4fd7-8981-27eef461dab1","resolution":{"observed_at":"2026-08-06T17:09:29.406406Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:28.838642Z","title":null,"venue":null,"work_id":"64432330-a721-4454-b968-893201a362a2","year":2019},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.826654Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:c2ccf455a5ca951979286b663a0ede0dbb30682a0445787fce986f86b9889ca0","observation_id":"6779d3a0-3a3c-41f9-abbb-5bb45239db9a","resolution":{"observed_at":"2026-08-06T17:09:28.994522Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:28.630625Z","title":"”A hybrid deep meta-ensemble networks with application in electric utility industry load forecasting.” Information Sciences 544 (2021): 183-196","venue":null,"work_id":"985e852b-09d9-4879-80fb-dca0e3c45c7c","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.888141Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:b07cc401912efc2a4e4905b7f4d5cf1bf2cc3043363b544d522397f21e045114","observation_id":"7aff70bc-78df-4456-a14b-4945387f2dcc","resolution":{"observed_at":"2026-08-06T17:09:28.724784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:28.461693Z","title":null,"venue":null,"work_id":"261a1b8e-b393-4213-86a7-5d3944673168","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:13.958475Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:1f3f3d96ac86a94e45617ab10bcc4fe4e0ad06240a30cab2779f4c4400f497e7","observation_id":"f9117e47-d02e-41bf-ad2d-4e7159a62870","resolution":{"observed_at":"2026-08-06T17:09:28.534977Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:28.221542Z","title":"Assessing the performance of deep learning mod- els for multivariate probabilistic energy forecasting.” Applied Energy 285 (2021): 116405","venue":null,"work_id":"5a78e37e-3cb4-4a45-bb48-32acbac2dd5e","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.020272Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:ba93bf5e485f1ad3bf8b99ee926979458c5eef7fe7ffd3eaeaf9d1949f9b0d07","observation_id":"b5da6440-7d33-4b31-ada0-bd3a90ba81ef","resolution":{"observed_at":"2026-08-06T17:09:28.360801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:27.962158Z","title":"”LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns.” IEEE Transactions on neural networks and learning systems 32, no","venue":null,"work_id":"f1ace01f-32d7-4768-aeac-317060563573","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.098041Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:231c03ac4a19d6f45d544e5713454e69f3d9bfe52adae97a4035dcd7d9bc6559","observation_id":"b8a4313a-a37a-41fd-9c18-4c90d2e28379","resolution":{"observed_at":"2026-08-06T17:09:28.113574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:27.713565Z","title":"”Cri- teria for classifying forecasting methods.” International Journal of Fore- casting 36, no","venue":null,"work_id":"bb698f7f-328d-4305-8416-8ba3d6abb93b","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.171870Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:4963d10e85cfa40e313781be74f2601cd25483e05277df680718b118f6a52190","observation_id":"534e6cff-e7af-4720-95b1-bc088d494d5b","resolution":{"observed_at":"2026-08-06T17:09:27.822921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:27.510450Z","title":"”Re- current neural networks for time series forecasting: Current status and future directions.” International Journal of Forecasting 37, no","venue":null,"work_id":"8d739e34-192e-4052-9cd4-f44df1dcf727","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.230407Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:e4bea4861741c4e0eeeffda2370c34e997eb07fcc4fa39c3aeb17d32bdfa09fd","observation_id":"4dd47036-0824-4f16-a061-c8249eddd8c6","resolution":{"observed_at":"2026-08-06T17:09:27.613281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:27.280631Z","title":"”Deciding When to Use a Personalized Model for Load Forecasting.” IEEE Transactions on Smart Grid (2024)","venue":null,"work_id":"c1163f5b-7fc5-4c29-b1c2-9285b86ffba4","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.312808Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:0e481b78f15e8b4b80e603881f1a508dcad0883431f6aa0362049ea3c3c02fa2","observation_id":"432648b8-a765-48af-bd96-f62fc4841f5a","resolution":{"observed_at":"2026-08-06T17:09:27.382654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06394","last_updated":"2024-11-10T08:51:49Z","snapshot_observed_at":"2026-07-06T19:48:00.661234Z","submitted_at":"2024-11-10T08:51:49Z","title":"Local vs. Global Models for Hierarchical Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.06394","snapshot_observed_at":"2026-08-06T17:09:14.401501Z","title":"”Local vs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.401501Z"},"links":{"cited_paper":"/paper/2411.06394","citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:ff208fb35f98d9a8551923d0a5732df1dce2a7d1260d6125b1e78a8883bdf817","observation_id":"eca99a33-29c9-41c1-b0a6-9e13f3f8e2ec","resolution":{"observed_at":"2026-08-06T17:09:14.401501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:09:27.095355Z","title":"”Deep federated learning-based privacy- preserving wind power forecasting.” IEEE Access 11 (2022): 39521- 39530","venue":null,"work_id":"c44b5263-2496-4890-bbe5-1e7295370c95","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.451968Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:f7748747f8ef3d67c17bf4227e6cc676c23fe92e2dd3bd2e24ea613c3c00238a","observation_id":"a9af621c-9f95-4347-a00a-b0d75c80d1f3","resolution":{"observed_at":"2026-08-06T17:09:27.183346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:26.830585Z","title":"”Privacy-preserving federated learning for residential short-term load forecasting.” Applied energy 326 (2022): 119915","venue":null,"work_id":"d74c92ef-9871-4456-a8fd-1f6cdfe95d48","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.512973Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:d74a2b6a3aad5a8e6e47b043d299b48041004e04e3a213bba1d034925c71c3a0","observation_id":"8eb11bdf-2d03-41f1-92b8-f7c1b4a1956a","resolution":{"observed_at":"2026-08-06T17:09:26.980753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:26.370838Z","title":"”A global modeling framework for load forecasting in distribution networks.” IEEE Transactions on Smart Grid 14, no","venue":null,"work_id":"25f1c4b0-da71-4a02-9c9e-1eabef5a64c0","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.573157Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:4b16e37d3363d0e02faee7e3ec22190f04db24aacbe19f5adc793900ff016420","observation_id":"50efeac2-271a-42f6-a609-e9c86e9c681e","resolution":{"observed_at":"2026-08-06T17:09:26.608099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:26.213441Z","title":"”Time- series extreme event forecasting with neural networks at uber.” In In- ternational conference on machine learning, vol","venue":null,"work_id":"b707057a-c4c0-430e-9d41-1c51e270f890","year":2017},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.638141Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:23149704f70a039b425ed5d22f6fd16c42cc494affbf877dc2380fa1e3371430","observation_id":"d0a60534-35f8-456b-bcf1-ad9e50de9389","resolution":{"observed_at":"2026-08-06T17:09:26.297002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:25.876344Z","title":"”Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach.” Expert systems with applications 140 (2020): 112896","venue":null,"work_id":"e5beef7d-b36f-42c5-8671-a2713fc983c2","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.776481Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:0cb675880fe90af20dea9e3b5cc47d8516e88d4dc5c64ff5d7efb8c8c17aac7a","observation_id":"d63062cc-5dd9-429d-8b7a-494ebf99967f","resolution":{"observed_at":"2026-08-06T17:09:25.980519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:25.683383Z","title":"”Improving the accuracy of global forecasting models using time series data augmentation.” Pattern Recognition 120 (2021): 108148","venue":null,"work_id":"d4e405e8-ed19-44ed-860a-6797d23d8daf","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.859632Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:66b8e63e61e3731884fd0e10a695b44ee0746e27a2f15c91a48a779d335b9b29","observation_id":"5747beae-1d20-42aa-a013-d9f6d185c4be","resolution":{"observed_at":"2026-08-06T17:09:25.769318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:25.514315Z","title":"”Tam- ing local effects in graph-based spatiotemporal forecasting.” Advances in Neural Information Processing Systems 36 (2024)","venue":null,"work_id":"71af747c-82e9-407a-ba04-17ebded5bf84","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.920973Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:0b2e1d379c0bae8e1953cc4dbe4fde97b4a1b44db523ea31088c5d590d363566","observation_id":"911d16e3-798d-42f1-906b-d090baa4724f","resolution":{"observed_at":"2026-08-06T17:09:25.595770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:25.378288Z","title":null,"venue":null,"work_id":"be7acdbd-af10-4b1e-a2da-5d6b6c4e681b","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:14.972339Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:eda9873723c86926f0fb57e3c454bb03b93357cd402c4340f33fd8892c3cd1aa","observation_id":"c5a7a5d8-29ce-4208-a827-d0b9f62840fb","resolution":{"observed_at":"2026-08-06T17:09:25.439712Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:25.201411Z","title":"”DeepAR: Probabilistic forecasting with autoregressive recurrent networks.” International Journal of Forecasting 36, no","venue":null,"work_id":"206e35b0-d287-4c6c-8448-ccb046349267","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.034569Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:9986dfd4defe7d7955b96dd768c8fbae267f1325642efc16d7ea183902222ea2","observation_id":"5d88beb4-14c5-406f-bf45-513ffe3e6794","resolution":{"observed_at":"2026-08-06T17:09:25.297795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.954859Z","title":"”When and How to Use Global Forecasting Methods on Heterogeneous Datasets.” Available at SSRN 4629272 (2023)","venue":null,"work_id":"95765ed0-8b2e-4e1e-ba5e-fbe39bfdace2","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.107052Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:768f28adcf662bda8d0f3e6a6f6901bf543101707d705430f71a9023072852dd","observation_id":"0db09121-1a77-41be-9d46-f360c0f59871","resolution":{"observed_at":"2026-08-06T17:09:25.021073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15978","last_updated":"2025-06-06T10:55:23Z","snapshot_observed_at":"2026-08-04T17:52:13.667624Z","submitted_at":"2023-10-24T16:26:38Z","title":"Graph Deep Learning for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15978","snapshot_observed_at":"2026-08-06T17:09:15.166931Z","title":"”Graph deep learning for time series forecasting.” arXiv preprint arXiv:2310.15978 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.166931Z"},"links":{"cited_paper":"/paper/2310.15978","citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:dbca75632b3a2c87ab52ad3378891ce4a36b089806a81a8006882094b91bfe10","observation_id":"a6d291d5-3456-49c8-a1ee-b4828d3f59d4","resolution":{"observed_at":"2026-08-06T17:09:15.166931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:09:26.059773Z","title":"”Handling concept drift in global time series forecasting.” In Forecasting with Artificial Intelligence: Theory and Applications, pp","venue":null,"work_id":"58f8295d-2a29-4200-b69f-1615d59d1b6c","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.239823Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:75ceded051b5677ed745eed6f66c2807bcfa2a1092a0ae1c442f56146b183278","observation_id":"622a7687-9216-4115-932a-d64beb8c0aa5","resolution":{"observed_at":"2026-08-06T17:09:26.121344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.807441Z","title":"Webb, Slawek Smyl, and Christoph Bergmeir","venue":null,"work_id":"3cd8b79a-69e2-476c-9597-395b9aa49855","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.312782Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:77f85745ec0aa111a124e874daf4f8f20a4a5f7250def8bd93db6c2fb18c5d50","observation_id":"83b89481-bc80-4b27-bac4-02524e6b868c","resolution":{"observed_at":"2026-08-06T17:09:24.880413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.675290Z","title":null,"venue":null,"work_id":"cf84ed65-2ed1-44ad-8ed9-06a4dc098d63","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.381433Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:81901ca867d923c078f6998dd130512f79f3ea757af4619a81286734baacefef","observation_id":"9646f74d-1b68-4453-b44c-3cb9fab33209","resolution":{"observed_at":"2026-08-06T17:09:24.740829Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.472713Z","title":"”Closed- loop Clustering-based Global Bandwidth Prediction in Real-Time Video Streaming.” IEEE Transactions on Machine Learning in Communica- tions and Networking, vol","venue":null,"work_id":"14fad4ae-3add-401f-b88c-3a5fc7b98fc8","year":2025},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.447650Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:d30808e3a01c2f1ebcfd8de5153e70b70cd4c6f2fa1c6dd355a6252b4084755f","observation_id":"70be3f0e-8a9f-4571-8099-3798c00d3775","resolution":{"observed_at":"2026-08-06T17:09:24.578241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.302272Z","title":null,"venue":null,"work_id":"fd5c75f3-9865-4fb3-81fd-c9771befa42d","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.494812Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:57d2a2868675495d96832a6ebcd797139d3592c00efb41c5c9bb3642576b36be","observation_id":"01b59e19-4ed7-4155-9fc9-4c120f71803c","resolution":{"observed_at":"2026-08-06T17:09:24.364390Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:24.135895Z","title":null,"venue":null,"work_id":"cc960377-f171-44a4-b2fc-b952e680514b","year":2018},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.547276Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:d2518a863f3b347017afd24b43d8f43bb58ea6653ae7a6b645ace3b4031e2459","observation_id":"fb13eaf1-22c0-41c8-82ec-e5c3f26e53cc","resolution":{"observed_at":"2026-08-06T17:09:24.219131Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:23.973816Z","title":"”Specialist vs generalist: A transformer architecture for global forecasting energy time series.” In 2022 15th In- ternational Conference on Human System Interaction (HSI), pp","venue":null,"work_id":"93e1a7c8-f349-4a4e-bd63-165cbe9453bc","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.594210Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:2edef3dc9be7ad4140251ca1cf1986e2028e7c29e4a7528f1f66bb28ab2d6747","observation_id":"9035e80a-9d9e-4ca7-b89d-6d23c069bca1","resolution":{"observed_at":"2026-08-06T17:09:24.052973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:23.719730Z","title":"”Next-day Load Forecasting with Smart Meter Data Using Global Recurrent Neural Networks.”","venue":null,"work_id":"d77beaee-0136-4062-9049-989caa5b4120","year":null},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.664560Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:c319a70fbdf9b20c340974168e30e8591d30ff1b8dd83dd1c71b9534bd170aa7","observation_id":"43b4f545-acd2-4c25-b112-9219078ac378","resolution":{"observed_at":"2026-08-06T17:09:23.871566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:23.406758Z","title":"”Deep learning for household load forecasting—A novel pooling deep RNN.” IEEE Transactions on Smart Grid 9, no","venue":null,"work_id":"3c6cc9bb-9dcb-41d7-9bf1-6e24f52981eb","year":2017},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.728613Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:df5aa65033eb26b2d650b5e158982b4961cd3230fbb04af7efc16b7bbe645f5d","observation_id":"c02f23a5-6e0f-44e3-ba99-032fba7a4ac6","resolution":{"observed_at":"2026-08-06T17:09:23.514694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:23.167838Z","title":"”Short-term forecasting of individual residential load based on deep learning and K- means clustering.” CSEE Journal of Power and Energy Systems 7, no","venue":null,"work_id":"dcfaae35-2961-4e6a-bacf-35ec22786fc5","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.787595Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:f68d1ae703057b1532cb3c106dece01a36c0d761461de420417f4f6e43b57fa7","observation_id":"36da6ab4-a25c-462c-96a0-bfd8203bbbd2","resolution":{"observed_at":"2026-08-06T17:09:23.312215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:22.889780Z","title":"”Individual load forecasting for multi-customers with distribution-aware temporal pooling.” In IEEE 60 INFOCOM 2021-IEEE Conference on Computer Communications, pp","venue":null,"work_id":"118bb4f0-382f-47ea-add8-881cffac260b","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.859381Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:22777af4c5c5e1082b24b4d61a631a08b13dc67f64f91491150f4ca4778963b5","observation_id":"9c6df94d-1af2-4589-bdc1-5b463a119d28","resolution":{"observed_at":"2026-08-06T17:09:23.003826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:22.749795Z","title":"”Residential load forecasting based on LSTM fusing self-attention mechanism with pooling.” Energy 229 (2021): 120682","venue":null,"work_id":"d95361d6-77d5-477b-a55d-9e2a8fd9f7cb","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.905565Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:c663c15de4693abddbc6e9896d98bc7942e9322b24440f2b41554f01c9b17d7b","observation_id":"525971f1-a433-49a7-9482-90942ec1ef1f","resolution":{"observed_at":"2026-08-06T17:09:22.812733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:22.588157Z","title":"”Individual load forecasting for multi-customers with distribution-aware temporal pooling.” In IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp","venue":null,"work_id":"3514e4f3-a5b2-48ad-8cca-1955fd85d6f0","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:15.948528Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:1942fea22da778f865a45359c4583c3804892452681731afd9ea15996f3419a9","observation_id":"4a0f529c-efc8-4b10-85cb-b81e1f638dae","resolution":{"observed_at":"2026-08-06T17:09:22.678369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:29.147419Z","title":"”Temporal data pooling with meta- initialization for individual short-term load forecasting.” IEEE Transac- tions on Smart Grid 14, no","venue":null,"work_id":"f9367b2d-c35c-4db9-9387-405d9ea3431c","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.018107Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:35ec4dcb46e64cda264ebc548df5735d3bd8d1aa6272a96be3bedcb9e77cae31","observation_id":"155f72f6-d364-4266-9ebf-924d9823e597","resolution":{"observed_at":"2026-08-06T17:09:29.235157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:22.262692Z","title":"”Personalized federated learning with theoretical guarantees: A model-agnostic meta- learning approach.” Advances in neural information processing systems 33 (2020): 3557-3568","venue":null,"work_id":"9927dd46-2ef0-45af-9173-6757be9f7a0d","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.083206Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:ff47f1892fef17a8b7380026fff0bb205a9de56e4ff27a201cf5023aca75e7c0","observation_id":"1777299b-06ac-44e9-9114-e4d47027f36d","resolution":{"observed_at":"2026-08-06T17:09:22.475714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:22.065828Z","title":"”Personalized feder- ated learning with moreau envelopes.” Advances in neural information processing systems 33 (2020): 21394-21405","venue":null,"work_id":"741f1e02-f48f-4070-9d35-4ba18bc63924","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.162458Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:a095f003a327c5630180c33b94dbebbbc20c42d43aaf4e32355f4fdc4f63688d","observation_id":"3a3ea8ea-17ba-4111-a822-5db27989b80e","resolution":{"observed_at":"2026-08-06T17:09:22.147213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-07-06T08:41:25.156353Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-06T17:09:16.251586Z","title":"”Federated learning with personalization layers.” arXiv preprint arXiv:1912.00818 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.251586Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:63f31da7475135f8a884b2ca001175552fe4f40b6720eddd148b3a7d6af5be15","observation_id":"05cfddcb-03b6-4951-b4b1-4f3e2d0576c2","resolution":{"observed_at":"2026-08-06T17:09:16.251586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:09:21.875246Z","title":"”A transformer-based method of multienergy load fore- casting in integrated energy system.” IEEE Transactions on Smart Grid 13, no","venue":null,"work_id":"2be36bed-49cc-4a6f-a781-6854b17cbcf0","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.362058Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:1eab64e3788bde0fea792a2770c1273ac7eec44fcf4b8267139acd198798298d","observation_id":"87300aed-4a4b-4f1c-8b0e-880530fc46d0","resolution":{"observed_at":"2026-08-06T17:09:21.952742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:21.745974Z","title":"”A global modeling framework for load forecasting in distribution networks.” IEEE Transactions on Smart Grid 14, no","venue":null,"work_id":"9070e12b-0a02-45d7-8b0a-8a3005cfa36f","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.466556Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:193bb8d82df169f782b446a5669585efa9de84623ca1bc59cc899265ec104464","observation_id":"583d9e6d-b2f2-4205-9aee-62b34911d976","resolution":{"observed_at":"2026-08-06T17:09:21.801728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:21.506181Z","title":"”Wielding Occam’s razor: Fast and frugal retail forecast- ing.” Journal of the Operational Research Society (2024): 1-20","venue":null,"work_id":"847ca45b-1e69-4024-9424-4eb562312cf2","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.557330Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:73d737cc0628f4aa1bde12e0379c9840b57785b5d4d6b3f2655d670842e56cc9","observation_id":"95ea189c-7dc3-48ff-a5fc-416da2460955","resolution":{"observed_at":"2026-08-06T17:09:21.644767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:21.240259Z","title":"”Time-series clustering–a decade review.” Information systems 53 (2015): 16-38","venue":null,"work_id":"dfca4b17-3082-4036-9b9f-1c3e7bd94df3","year":2015},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.649485Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:45d6bc9accb57cf0409bc15c0d9548f8c2063e5a93f32552bf3c2fcc6754f7a2","observation_id":"99e34ff3-ee54-4f25-9d3e-401adc620284","resolution":{"observed_at":"2026-08-06T17:09:21.380608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:20.989515Z","title":"Hourly Load by Area and Region","venue":null,"work_id":"81e26aae-8003-40d0-a3c2-1f1f088a2441","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.765490Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:459781073e31bef29ca758c6b820d74ae52b5af75a240b45e40e8e914b3d67d9","observation_id":"69be582a-4128-4e5d-8f4d-5790e4776470","resolution":{"observed_at":"2026-08-06T17:09:21.123699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:20.672628Z","title":"Last modified June 23, 2020","venue":null,"work_id":"b3d41530-798f-4c10-b8c3-297455deb862","year":2020},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:16.861057Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:f44800544b1611c3b1b44e039611e831f7a64df2651827904977b4beaeab0dc3","observation_id":"4b00a98a-7f47-49b1-8fbb-30aa18f7670c","resolution":{"observed_at":"2026-08-06T17:09:20.796711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:20.363598Z","title":"Accessed November 12, 2024","venue":null,"work_id":"293de140-c8b6-441e-b9e2-00f4300a7351","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.012511Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:1c57826cc17c5b5308004e0510498e86dbb8bdc8faccf236c47bc51195734aa9","observation_id":"ab5773ec-520d-40ea-bfb0-0c8e1194d872","resolution":{"observed_at":"2026-08-06T17:09:20.452717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:20.110264Z","title":"Accessed November 12, 2024","venue":null,"work_id":"b25a9266-38e2-4782-b8b4-87a16170c19e","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.172915Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:8d123d6480e70111f1ce1cba0295f204faee4e4b5529183f69fd27182c6a3afb","observation_id":"7d00db9d-842d-479e-a854-6df10d282878","resolution":{"observed_at":"2026-08-06T17:09:20.209471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.886063Z","title":"”Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis.” IEEE Transactions on Knowledge and Data Engineering (2024)","venue":null,"work_id":"11763081-8488-4de5-9fc2-5eb8c449e447","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.339041Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:42158d0d51f256b758b4d964be4ee72b894df04e954836e3fe68c6b50014a7fe","observation_id":"7f51ff62-346b-4292-9a21-7b69917b00cd","resolution":{"observed_at":"2026-08-06T17:09:20.014915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.656156Z","title":null,"venue":null,"work_id":"244c321f-da92-4493-a810-87c557a7fd95","year":2024},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.557801Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:0a4cfae9152444c9fc7e18a38bf896695531f11c5d4da477841ddf7c3d006a8d","observation_id":"213ddee5-8732-4346-89b7-a2466fa8c2a9","resolution":{"observed_at":"2026-08-06T17:09:19.728946Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.442118Z","title":"”Benchmarking robustness of load forecasting models under data integrity attacks.” International Journal of Forecasting 34, no","venue":null,"work_id":"116160cb-17f6-4c19-8077-cf57df8c54c2","year":2018},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.753265Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:2899c67ba02e3d61887b3b996d951288b00475008a6dcfbba3dca98097019d80","observation_id":"4bf7d0b9-6eb2-4d1e-8b42-dc35cf263930","resolution":{"observed_at":"2026-08-06T17:09:19.555966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.314554Z","title":"Core Concepts and Methods in Load Forecasting: With Applications in Distribution Networks","venue":null,"work_id":"78573761-1e89-48ec-ae58-c2788f6a6f01","year":2023},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:17.920003Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:50529f4813b5ad879f3d517a1d354b562b3336929e3b2b67410b18ca3d21578d","observation_id":"f8fac684-5b61-4417-8010-8cb6dca8b4a9","resolution":{"observed_at":"2026-08-06T17:09:19.372303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.278171Z","title":"”Analysis and clustering of residential customers energy behavioral demand using smart meter data.” IEEE transactions on smart grid 7, no","venue":null,"work_id":"98c56086-400f-4e51-8cd4-c43881a4bd92","year":2015},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.062857Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:6e4268802f48a1bd4f122f90aaa7515e9dea1590a4bbc74ecae26ce76579d3e5","observation_id":"d0d933cf-4836-480b-8cdb-8811ed9e3d7e","resolution":{"observed_at":"2026-08-06T17:09:19.282254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.205089Z","title":"Energy Information Administration","venue":null,"work_id":"93c31ba2-64e3-489b-88a3-90f575acffbe","year":null},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.154543Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:189838f43c4638326554cde731b56778ec28705641413076c5dcb6fe6d4b8d91","observation_id":"94edf060-0527-4bfd-8899-07d1ff2b1a9b","resolution":{"observed_at":"2026-08-06T17:09:19.257807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:18.893852Z","title":"Taylor, and Rob J","venue":null,"work_id":"58c8ed2d-db3d-49c1-b819-d32d67846968","year":2017},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.294312Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:dca2597975c88f32073c5dbed838d10e74649fd3953e140c8e8305943a614da2","observation_id":"dc0c9027-26e6-4e5b-ae4f-7f39fa231d5f","resolution":{"observed_at":"2026-08-06T17:09:18.960911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:18.721808Z","title":"”Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting.” Expert Systems with Applications 175 (2021): 114844","venue":null,"work_id":"e6d40e8d-5735-4472-b634-a985b75dc4e3","year":2021},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.375261Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:defdc542dc6ba717d0b7deb66179c1856ad1e06130b84e00763a4ede5b602c1e","observation_id":"05c75203-2852-438c-807d-e8961db1c02c","resolution":{"observed_at":"2026-08-06T17:09:18.829044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:18.563287Z","title":"”Mul- tivariate empirical mode decomposition based hybrid model for day- ahead peak load forecasting.” Energy 239 (2022): 122245","venue":null,"work_id":"6846a8b8-1dd3-4893-b288-ea9c56b53058","year":2022},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.428363Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:493f7a28faccece081569988594ae98033551f4d77440edcf049f019aeb60d3c","observation_id":"5e83aadb-f55b-4279-8c86-b1aa00cb59b2","resolution":{"observed_at":"2026-08-06T17:09:18.635537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:09:19.046511Z","title":null,"venue":null,"work_id":"8b9fac50-f304-401c-81a4-b0efb43dcaa1","year":null},"citing_paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-06T17:09:18.219897Z"},"links":{"citing_paper":"/paper/2507.11729"},"observation_digest":"sha256:35b1aef8772f0a29d90ff173ddc96921090352c76b0454a196feab6e4b14a579","observation_id":"fb70df02-171d-4d6b-812e-026b6db82547","resolution":{"observed_at":"2026-08-06T17:09:19.121829Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.11729","last_updated":"2025-07-15T20:58:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T04:23:11.297600Z","submitted_at":"2025-07-15T20:58:14Z","title":"Globalization for Scalable Short-term Load Forecasting"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":47},"total_outbound_references":59},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2507.11729."}