{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NHVCBEDBBBRG723IV22QJZACPO","short_pith_number":"pith:NHVCBEDB","canonical_record":{"source":{"id":"2212.09030","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-18T07:42:48Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"3d4e25967d2ef249da3a8de9937ac3c6c7ad541b7deb3264af872ea88da3706a","abstract_canon_sha256":"7909daa2e9b3b2171caa0ddff3dc1da00aa0d8296e3abf3e7e23b3a3f5defa81"},"schema_version":"1.0"},"canonical_sha256":"69ea20906108626feb68aeb504e4027b87726ba235ebdd5c2e0378a637f54e6d","source":{"kind":"arxiv","id":"2212.09030","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09030","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09030v1","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09030","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_12","alias_value":"NHVCBEDBBBRG","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_16","alias_value":"NHVCBEDBBBRG723I","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_8","alias_value":"NHVCBEDB","created_at":"2026-07-05T05:26:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NHVCBEDBBBRG723IV22QJZACPO","target":"record","payload":{"canonical_record":{"source":{"id":"2212.09030","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-18T07:42:48Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"3d4e25967d2ef249da3a8de9937ac3c6c7ad541b7deb3264af872ea88da3706a","abstract_canon_sha256":"7909daa2e9b3b2171caa0ddff3dc1da00aa0d8296e3abf3e7e23b3a3f5defa81"},"schema_version":"1.0"},"canonical_sha256":"69ea20906108626feb68aeb504e4027b87726ba235ebdd5c2e0378a637f54e6d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:17.199400Z","signature_b64":"Fw0EF421tymeg4PP0I3faOm+HTzucPTJJBEDcWy8nAH9CqnRBYDT6zSB9lkk0Xl9A0RTFiVbnQz8Vpt08LvbCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69ea20906108626feb68aeb504e4027b87726ba235ebdd5c2e0378a637f54e6d","last_reissued_at":"2026-07-05T05:26:17.199044Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:17.199044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.09030","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":"2026-07-05T05:26:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7YgnZvThD2tndUXeXA25TdU95jCgzLc4KknTDL4ZcDoqqRp57b9gZ51eK1ICYSS+inn7GWX8wgZI4AOl8ULvDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T21:09:02.277875Z"},"content_sha256":"7f99687f977664992ddd659062e8e41e2b3a58e0fa44d27c7c931b2329c7f499","schema_version":"1.0","event_id":"sha256:7f99687f977664992ddd659062e8e41e2b3a58e0fa44d27c7c931b2329c7f499"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NHVCBEDBBBRG723IV22QJZACPO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Grzegorz Dudek, Pawe{\\l} Pe{\\l}ka, Slawek Smyl","submitted_at":"2022-12-18T07:42:48Z","abstract_excerpt":"In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09030","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/2212.09030/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":"2026-07-05T05:26:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r//hgNXFtzXYTzXKEvBsqxmbQjd8uMTPubh2oLnSnn1mvDptj0eh/ZSZJyWw6Mg6YHgSoOW+tGJCW2xzu/vICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T21:09:02.278532Z"},"content_sha256":"0a9ba446db5a207e4836ba4c2602defe1739761d599e47e3c619ca97411f8b64","schema_version":"1.0","event_id":"sha256:0a9ba446db5a207e4836ba4c2602defe1739761d599e47e3c619ca97411f8b64"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NHVCBEDBBBRG723IV22QJZACPO/bundle.json","state_url":"https://pith.science/pith/NHVCBEDBBBRG723IV22QJZACPO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NHVCBEDBBBRG723IV22QJZACPO/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":"2026-08-19T21:09:02Z","links":{"resolver":"https://pith.science/pith/NHVCBEDBBBRG723IV22QJZACPO","bundle":"https://pith.science/pith/NHVCBEDBBBRG723IV22QJZACPO/bundle.json","state":"https://pith.science/pith/NHVCBEDBBBRG723IV22QJZACPO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NHVCBEDBBBRG723IV22QJZACPO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NHVCBEDBBBRG723IV22QJZACPO","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":"7909daa2e9b3b2171caa0ddff3dc1da00aa0d8296e3abf3e7e23b3a3f5defa81","cross_cats_sorted":["cs.AI","cs.NE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-18T07:42:48Z","title_canon_sha256":"3d4e25967d2ef249da3a8de9937ac3c6c7ad541b7deb3264af872ea88da3706a"},"schema_version":"1.0","source":{"id":"2212.09030","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09030","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09030v1","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09030","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_12","alias_value":"NHVCBEDBBBRG","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_16","alias_value":"NHVCBEDBBBRG723I","created_at":"2026-07-05T05:26:17Z"},{"alias_kind":"pith_short_8","alias_value":"NHVCBEDB","created_at":"2026-07-05T05:26:17Z"}],"graph_snapshots":[{"event_id":"sha256:0a9ba446db5a207e4836ba4c2602defe1739761d599e47e3c619ca97411f8b64","target":"graph","created_at":"2026-07-05T05:26:17Z","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/2212.09030/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchi","authors_text":"Grzegorz Dudek, Pawe{\\l} Pe{\\l}ka, Slawek Smyl","cross_cats":["cs.AI","cs.NE"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-18T07:42:48Z","title":"Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09030","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":"sha256:7f99687f977664992ddd659062e8e41e2b3a58e0fa44d27c7c931b2329c7f499","target":"record","created_at":"2026-07-05T05:26:17Z","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":"7909daa2e9b3b2171caa0ddff3dc1da00aa0d8296e3abf3e7e23b3a3f5defa81","cross_cats_sorted":["cs.AI","cs.NE"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-18T07:42:48Z","title_canon_sha256":"3d4e25967d2ef249da3a8de9937ac3c6c7ad541b7deb3264af872ea88da3706a"},"schema_version":"1.0","source":{"id":"2212.09030","kind":"arxiv","version":1}},"canonical_sha256":"69ea20906108626feb68aeb504e4027b87726ba235ebdd5c2e0378a637f54e6d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69ea20906108626feb68aeb504e4027b87726ba235ebdd5c2e0378a637f54e6d","first_computed_at":"2026-07-05T05:26:17.199044Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:26:17.199044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fw0EF421tymeg4PP0I3faOm+HTzucPTJJBEDcWy8nAH9CqnRBYDT6zSB9lkk0Xl9A0RTFiVbnQz8Vpt08LvbCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:26:17.199400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.09030","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f99687f977664992ddd659062e8e41e2b3a58e0fa44d27c7c931b2329c7f499","sha256:0a9ba446db5a207e4836ba4c2602defe1739761d599e47e3c619ca97411f8b64"],"state_sha256":"908dedc58e7c4309598f98fc1e8cb161e1d6dfd0fe593b65762c023c27fb1047"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dXJMkGitYtfJGuRlhn8rP0kkuQkxAMZr+8bQCySpfjFEBBouoK3QU/zYc8yWEVzv6g/a3m7dBUZ/p282JRCjAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T21:09:02.283151Z","bundle_sha256":"b37651c19b6eb3f4770a1e6d0eee1104ccb6e01aeffc7636691e72b7d35faade"}}