{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:WQXCKKNFDFTSKQSD3G2YOWFOVQ","short_pith_number":"pith:WQXCKKNF","canonical_record":{"source":{"id":"2205.03571","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-07T06:42:48Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5cb9b6d6a227a909f52156ffdc1a59360f20a25eb995d5ac363827f526fb8c0d","abstract_canon_sha256":"7892fad208758feb8c0d50e37dd3c499b6f2742270fa5c37fd1119423f9c8805"},"schema_version":"1.0"},"canonical_sha256":"b42e2529a51967254243d9b58758aeac30d518a546e2abb83541be911707a71f","source":{"kind":"arxiv","id":"2205.03571","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.03571","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"arxiv_version","alias_value":"2205.03571v1","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.03571","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_12","alias_value":"WQXCKKNFDFTS","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_16","alias_value":"WQXCKKNFDFTSKQSD","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_8","alias_value":"WQXCKKNF","created_at":"2026-07-05T04:21:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:WQXCKKNFDFTSKQSD3G2YOWFOVQ","target":"record","payload":{"canonical_record":{"source":{"id":"2205.03571","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-07T06:42:48Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5cb9b6d6a227a909f52156ffdc1a59360f20a25eb995d5ac363827f526fb8c0d","abstract_canon_sha256":"7892fad208758feb8c0d50e37dd3c499b6f2742270fa5c37fd1119423f9c8805"},"schema_version":"1.0"},"canonical_sha256":"b42e2529a51967254243d9b58758aeac30d518a546e2abb83541be911707a71f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:21:15.875907Z","signature_b64":"XOp/i80luHqpL6Vx7ZluL1NYttHZY9Bjnd9K5CoPeP3qOMDgvFnCgBhUbDVfameGz2X4lPfqZCZudtmsAsuDAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b42e2529a51967254243d9b58758aeac30d518a546e2abb83541be911707a71f","last_reissued_at":"2026-07-05T04:21:15.875475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:21:15.875475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.03571","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:21:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ivt8uPzBg13ZE/KwUiD2uzO451CXAVgKr/9GPYLGnJ1TTQxAlA/Cikj+UVjxjO9TSf8NE8DU6pceLPk5+ZFCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T00:43:27.618260Z"},"content_sha256":"316fc75348e81f32d8235dfaf067e1c50b912227f4c44d77050d5cb246aa4ab2","schema_version":"1.0","event_id":"sha256:316fc75348e81f32d8235dfaf067e1c50b912227f4c44d77050d5cb246aa4ab2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:WQXCKKNFDFTSKQSD3G2YOWFOVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep learning for spatio-temporal forecasting -- application to solar energy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Vincent Le Guen","submitted_at":"2022-05-07T06:42:48Z","abstract_excerpt":"This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.03571","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.03571/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:21:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GToF0RLM+fz0mvOAj9U4yi/djOVMmd4L57nhZ0ky6m19jzCFuJ5cFvf5qJ8X1t5ND0LCQxNTqgGT39D5x5n+Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T00:43:27.618650Z"},"content_sha256":"efa5c14b5e91a0fb0abb500e58a84c4a7c20cdc58a18a1afd94c393e131f13d9","schema_version":"1.0","event_id":"sha256:efa5c14b5e91a0fb0abb500e58a84c4a7c20cdc58a18a1afd94c393e131f13d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/bundle.json","state_url":"https://pith.science/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-17T00:43:27Z","links":{"resolver":"https://pith.science/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ","bundle":"https://pith.science/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/bundle.json","state":"https://pith.science/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WQXCKKNFDFTSKQSD3G2YOWFOVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WQXCKKNFDFTSKQSD3G2YOWFOVQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7892fad208758feb8c0d50e37dd3c499b6f2742270fa5c37fd1119423f9c8805","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-07T06:42:48Z","title_canon_sha256":"5cb9b6d6a227a909f52156ffdc1a59360f20a25eb995d5ac363827f526fb8c0d"},"schema_version":"1.0","source":{"id":"2205.03571","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.03571","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"arxiv_version","alias_value":"2205.03571v1","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.03571","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_12","alias_value":"WQXCKKNFDFTS","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_16","alias_value":"WQXCKKNFDFTSKQSD","created_at":"2026-07-05T04:21:15Z"},{"alias_kind":"pith_short_8","alias_value":"WQXCKKNF","created_at":"2026-07-05T04:21:15Z"}],"graph_snapshots":[{"event_id":"sha256:efa5c14b5e91a0fb0abb500e58a84c4a7c20cdc58a18a1afd94c393e131f13d9","target":"graph","created_at":"2026-07-05T04:21:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.03571/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the","authors_text":"Vincent Le Guen","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-07T06:42:48Z","title":"Deep learning for spatio-temporal forecasting -- application to solar energy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.03571","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:316fc75348e81f32d8235dfaf067e1c50b912227f4c44d77050d5cb246aa4ab2","target":"record","created_at":"2026-07-05T04:21:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7892fad208758feb8c0d50e37dd3c499b6f2742270fa5c37fd1119423f9c8805","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-07T06:42:48Z","title_canon_sha256":"5cb9b6d6a227a909f52156ffdc1a59360f20a25eb995d5ac363827f526fb8c0d"},"schema_version":"1.0","source":{"id":"2205.03571","kind":"arxiv","version":1}},"canonical_sha256":"b42e2529a51967254243d9b58758aeac30d518a546e2abb83541be911707a71f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b42e2529a51967254243d9b58758aeac30d518a546e2abb83541be911707a71f","first_computed_at":"2026-07-05T04:21:15.875475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:21:15.875475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XOp/i80luHqpL6Vx7ZluL1NYttHZY9Bjnd9K5CoPeP3qOMDgvFnCgBhUbDVfameGz2X4lPfqZCZudtmsAsuDAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:21:15.875907Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.03571","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:316fc75348e81f32d8235dfaf067e1c50b912227f4c44d77050d5cb246aa4ab2","sha256:efa5c14b5e91a0fb0abb500e58a84c4a7c20cdc58a18a1afd94c393e131f13d9"],"state_sha256":"6848d873f337f376eb06cb7e4861eb55e45662df856f9d0ddf722973d57f88b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Zjfkq65AX7r/iYtr72/lOI3xC+xjLZOLyws5s8b42nmG9HhjNo8Ry4kgzWEUlua0ChQCIubJGl0fZveAGv0Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T00:43:27.620779Z","bundle_sha256":"609a151e50fc43c58e90b83d16bf7b6f598faccc68166b97c8fb68a03113eaf9"}}