{"as_of":"2026-08-18T01:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1d16413632ab3d9398589673ff03fb0766d201b25e367cfbc124f7b0a942842","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:39:52.649467Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.06657/citation-record","integrity":"/paper/2505.06657/integrity","json":"/paper/2505.06657/citation-record.json","paper":"/paper/2505.06657"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:39:52.952933Z","title":"Global EV outlook 2024,","venue":null,"work_id":"4fae29f7-6fba-41be-9289-6853e090c887","year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.552696Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:d5719fd721d570376606c981f09ba6d3c8f83405e8fe0823ec9fc78f89ec1042","observation_id":"c1a7c0c7-1bf3-421b-90ce-3804d234620e","resolution":{"observed_at":"2026-08-15T22:39:52.956258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.942226Z","title":"Electric vehicle charging load forecasting: A comparative studyofdeeplearningapproaches,","venue":null,"work_id":"fe117dfa-a39b-45cd-b353-001dab254530","year":2019},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.557326Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:fe2bde7057a04f8501290db1434390da41eee73120e0b64f5ce06c5f8fe046d1","observation_id":"43f48edd-b7f5-45b9-bca5-b54d53d3672b","resolution":{"observed_at":"2026-08-15T22:39:52.946029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.932527Z","title":"Elec- tric vehicle charging load forecasting considering weather impact,","venue":null,"work_id":"5e4a90f7-9791-4ba0-b460-096265dd46e6","year":2025},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.561099Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:34904bf317ea2b7f77e9b6df7778f3aaf72844cf03a09e4190208466dc55379b","observation_id":"71af130d-d708-476a-b6b4-108eb3315293","resolution":{"observed_at":"2026-08-15T22:39:52.936037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.921325Z","title":"A transfer learning method for electric vehicles charging strategy based ondeepreinforcementlearning,","venue":null,"work_id":"3903a96f-0f07-41ae-879a-da5b123d1d4e","year":2023},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.564591Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:c4237f5181e371192c6d92b48dc7270b1127ede87015677d52d33b909f20e55b","observation_id":"cce68fa8-ee35-4f87-9bc2-33272ca51173","resolution":{"observed_at":"2026-08-15T22:39:52.925342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.911892Z","title":"Review on schedul- ing,clustering,andforecastingstrategiesforcontrollingelectricvehi- cle charging: Challenges and recommendations,","venue":null,"work_id":"f952688d-02f4-4072-b745-1a3e1d3484bd","year":2019},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.568014Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:2630a3dd035f4213f2e7a8768b656e2de4df459ef4759642e6b1b7a73b18c0b7","observation_id":"27a7223a-e1e6-42d1-a437-35fecc792399","resolution":{"observed_at":"2026-08-15T22:39:52.915296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.902201Z","title":null,"venue":null,"work_id":"0aca3944-29cf-444b-af89-0d51b5f6b9a5","year":2000},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.572059Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:b2f5a5909d7bdf1c064e547240d5a2e9431d5c86f76cff228ed405b9509bbee3","observation_id":"ca5564a6-86b5-4b07-8f26-a0418b68bf5c","resolution":{"observed_at":"2026-08-15T22:39:52.905422Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.891252Z","title":"Informer:Beyondefficienttransformerforlongsequencetime-series forecasting,","venue":null,"work_id":"e6e07ee8-7f33-4ce6-9c01-fb290bd339a1","year":2021},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.575864Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:122a4c6f1133086ea04bc87f710345ae9aa757d8b16eb591907e1d2192b57d9d","observation_id":"ea4ab927-9a5c-4096-8f08-4a460f40f4f6","resolution":{"observed_at":"2026-08-15T22:39:52.895536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-08-17T01:22:50.943392Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-15T22:39:52.579308Z","title":"Atimeseries is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.579308Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:ce12441e410b9c0fc308be835cb31828ef9d90c64ad0a018d511fcacdf98212c","observation_id":"f9901aec-5a89-4475-8773-3d2538c1ce6a","resolution":{"observed_at":"2026-08-15T22:39:52.579308Z","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":"2026-08-15T22:39:52.880601Z","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,","venue":null,"work_id":"7d8e47cd-81d1-4402-a642-2376b88ab673","year":2021},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.583136Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:b6503525b34c1b8613e0f78f02bf101122d1f1baa339c46e16b2994468a56cb8","observation_id":"a6e9e86e-c91b-46ad-b584-cf52f29c0732","resolution":{"observed_at":"2026-08-15T22:39:52.885123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13504","last_updated":"2022-08-17T17:09:38Z","snapshot_observed_at":"2026-08-16T16:57:24.714082Z","submitted_at":"2022-05-26T17:17:08Z","title":"Are Transformers Effective for Time Series Forecasting?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13504","snapshot_observed_at":"2026-08-15T22:39:52.586307Z","title":"Aretransformerseffective fortimeseriesforecasting?","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.586307Z"},"links":{"cited_paper":"/paper/2205.13504","citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:7c882b5fa27cca1f79ed85b1ba1609d918e6f046b81cc5b1d0b1d76d6c69d70c","observation_id":"472bcea8-5788-401e-b069-ecf9ec6d32d1","resolution":{"observed_at":"2026-08-15T22:39:52.586307Z","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":"2026-08-15T22:39:52.870580Z","title":"Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,","venue":null,"work_id":"70b066a8-0f8f-4c5b-ae02-f0e179dbe954","year":2023},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.589862Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:e5df107ce645320da9be67a3847c5161dec07d62d7696598ee45ba3f5eba130e","observation_id":"812a8734-a99a-4ab7-8aff-d80a60dcc853","resolution":{"observed_at":"2026-08-15T22:39:52.874496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.860441Z","title":"Frequency-domain mlps are more effective learners in time series forecasting,","venue":null,"work_id":"7eca8ffc-01e9-4994-9542-aca850c8d022","year":2023},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.593019Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:a3b1df7629b2192432d6313f488366f1e0af7a0ec6f7794b43cbf68baef5ecd6","observation_id":"1a304217-e56c-43d3-944c-6943290f7ddd","resolution":{"observed_at":"2026-08-15T22:39:52.864292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-08-15T02:33:31.807561Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19756","snapshot_observed_at":"2026-08-15T22:39:52.596495Z","title":"Kan: Kolmogorov-arnold networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.596495Z"},"links":{"cited_paper":"/paper/2404.19756","citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:77a2bbd6180d6801bfa94080a24c01569a54386b9ccaa3e97b78e375204db729","observation_id":"1a6575e7-9f65-4216-9938-230f105827da","resolution":{"observed_at":"2026-08-15T22:39:52.596495Z","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":"2026-08-15T22:39:52.849963Z","title":"Mlp-mixer: An all-mlp architecture for vision,","venue":null,"work_id":"edbb11ee-ebe3-4019-a492-fa4eb79fc3a0","year":2021},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.600688Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:c75b3fa3fef602593902083132ca356bb084a37ed7c9becf7e1d45b26909f50d","observation_id":"e7bcad06-3011-4306-af16-4f6f387f81bd","resolution":{"observed_at":"2026-08-15T22:39:52.853796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.838008Z","title":"Transfer learning- based framework enhanced by deep generative model for cold-start forecasting of residential ev charging behavior,","venue":null,"work_id":"4897347c-08d4-40e9-a887-098ff43ff0dc","year":2023},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.604193Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:4d005435f949b3aa058425275bfce96a02621d8a53f4277470416c943fc426c2","observation_id":"95b83cad-ee02-43e1-9dfe-d12c4f7f1784","resolution":{"observed_at":"2026-08-15T22:39:52.842158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1207.0580","last_updated":"2012-07-03T06:35:15Z","snapshot_observed_at":"2026-08-15T00:54:35.320150Z","submitted_at":"2012-07-03T06:35:15Z","title":"Improving neural networks by preventing co-adaptation of feature detectors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1207.0580","snapshot_observed_at":"2026-08-15T22:39:52.608105Z","title":"Improving neural networks by preventing co- adaptation of feature detectors,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.608105Z"},"links":{"cited_paper":"/paper/1207.0580","citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:05889a8a7bdd689813eddcb5ef301adb8c84e36fd2e4fc45bc039538401e9019","observation_id":"5293ef0e-2d26-48e3-a01c-65ed8907cd53","resolution":{"observed_at":"2026-08-15T22:39:52.608105Z","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":"2026-08-15T22:39:52.826687Z","title":"Deep sparse rectifier neural networks,","venue":null,"work_id":"6ba583b8-3b95-4d96-800e-e5089e2404ca","year":2011},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.611795Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:3b3418bd75f501eabf896e0ae0f7bb40d7718f32775d3a26d48720955b2e491d","observation_id":"4df82f34-5ed6-4306-bb3f-c63634bd0cbd","resolution":{"observed_at":"2026-08-15T22:39:52.830601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.815074Z","title":"Multimodal machine learning: A survey and taxonomy,","venue":null,"work_id":"9f8cdddd-7c17-4335-9084-b5199d750bc5","year":2018},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.615320Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:2a2a4a1901aee364dfd43482e7e9ef9f36a62c0e3d9ec6a51f811b9ba342f78e","observation_id":"76283055-59fe-4f82-9b09-4107534f08a4","resolution":{"observed_at":"2026-08-15T22:39:52.818910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.802637Z","title":"Modeling long-and short-term temporal patterns with deep neural networks,","venue":null,"work_id":"9cffce30-d756-4d52-a42a-1835ec66da1a","year":2018},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.618984Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:aa7412ebcdaaf9790309ff04cd5fd2ebd98e41e5f04c52ecc4e15188cbcf82be","observation_id":"12537820-7cce-4057-8802-7e2f0461d26f","resolution":{"observed_at":"2026-08-15T22:39:52.807424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.791193Z","title":"In- former model with season-aware block for efficient long-term power time series forecasting,","venue":null,"work_id":"d23aca80-67ec-475e-880e-154352d143a2","year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.622919Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:7fd5f7272a54d22f66f99c72534f3c699822b163686f924d280a60d7b2d874e7","observation_id":"30fb90b3-33fc-4100-8aac-3ceff05ff398","resolution":{"observed_at":"2026-08-15T22:39:52.795171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.780252Z","title":"Multimodaljoint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network,","venue":null,"work_id":"318ae1ec-f4c1-4e26-a223-6f9a1a4565bc","year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.626811Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:e88e18ee9e2cc43ec16a55381d306625b51591425319742cb885750cc76c55c9","observation_id":"6e446f64-b806-4be0-90ca-62f8c7dcb04f","resolution":{"observed_at":"2026-08-15T22:39:52.784344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.768286Z","title":"Deep learning models for time series forecast- ing: a review,","venue":null,"work_id":"69b4090e-27c7-4280-8fd2-fa6442dd0f5d","year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.630929Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:a5c6d0e8e0cf60d2a4d0f4d12026177fc3b0ebcc31cebda0c49177c8704aaad4","observation_id":"7d9a5ee6-b216-43e3-8159-c6143fdb04b8","resolution":{"observed_at":"2026-08-15T22:39:52.772498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18959","last_updated":"2025-02-14T02:50:45Z","snapshot_observed_at":"2026-08-13T07:01:29.994147Z","submitted_at":"2025-01-31T08:33:10Z","title":"Enhancing Neural Function Approximation: The XNet Outperforming KAN","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18959","snapshot_observed_at":"2026-08-15T22:39:52.634675Z","title":"Enhancing neural function approxima- tion:Thexnetoutperformingkan,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.634675Z"},"links":{"cited_paper":"/paper/2501.18959","citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:8396c804596ca04f520f85d7d9413fe3168e300d68787036c8ce6d96540fc588","observation_id":"8f935489-1a93-4ac2-988e-0b964f87c26c","resolution":{"observed_at":"2026-08-15T22:39:52.634675Z","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":"2026-08-15T22:39:52.757356Z","title":"An improvised cubic b-spline collocation method for solving the nonlinear klein-gordon equation,","venue":null,"work_id":"99974016-9b89-4810-aa2b-639db9440a1e","year":2025},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.638470Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:52393edf1963ceb300580b023d4658154cf31c81ca3101d1518a725cbe34aee1","observation_id":"aa22bb61-2328-498a-bfbc-5f70e7f446ac","resolution":{"observed_at":"2026-08-15T22:39:52.761266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.745483Z","title":"Estimating production functions through additive models based on regression splines,","venue":null,"work_id":"a485dcd5-e54a-451e-b0a9-d7d8f2806149","year":2024},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.642318Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:e74f99631ebf415907c453478a09cb987c52d657728b270c8ddb4afbfc169ca2","observation_id":"8d89e6a5-f764-4fd6-92d2-b08d816667b4","resolution":{"observed_at":"2026-08-15T22:39:52.749984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.734696Z","title":"Electric vehicle charging station data,","venue":null,"work_id":"1089b631-22be-4dd5-8288-d9b3965385bf","year":null},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.646006Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:11db7d30826492fe0f8f56db707f891165ae90b711821351f0e7839e602c2c65","observation_id":"ea0d85c8-12b3-4ffa-9390-6afb25bebd25","resolution":{"observed_at":"2026-08-15T22:39:52.738660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:39:52.722317Z","title":"Available: https://open-data.bouldercolorado.gov/ datasets/95992b3938be4622b07f0b05eba95d4c_0/explore Z","venue":null,"work_id":"9d7a03d0-c837-4d7b-bf9e-5dcbf449d7ec","year":null},"citing_paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T22:39:52.649467Z"},"links":{"citing_paper":"/paper/2505.06657"},"observation_digest":"sha256:a8f42532518c057a5830f911aec632573715650785bb343027f597578a51a9ac","observation_id":"5bb43323-da7b-43e3-b69c-569efc469768","resolution":{"observed_at":"2026-08-15T22:39:52.727564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.06657","last_updated":"2025-05-10T14:11:12Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-15T22:34:34.707674Z","submitted_at":"2025-05-10T14:11:12Z","title":"Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":27},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.06657."}