{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WKLD5PY5XGD6IIEI2SZC4WKPPP","short_pith_number":"pith:WKLD5PY5","canonical_record":{"source":{"id":"2411.11350","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-18T07:39:46Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"1ea227d39a83e4f71ca3b618d9a0e2a3f8f8d44ff45cb6f1509d72b228ac12e7","abstract_canon_sha256":"aebc4feb705ddaa5eb040ddb16673f5d0d9b120a86b265298826f6865df2dd82"},"schema_version":"1.0"},"canonical_sha256":"b2963ebf1db987e42088d4b22e594f7bde4ea87727e3b246daee42c5c65733d3","source":{"kind":"arxiv","id":"2411.11350","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.11350","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"arxiv_version","alias_value":"2411.11350v2","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11350","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_12","alias_value":"WKLD5PY5XGD6","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_16","alias_value":"WKLD5PY5XGD6IIEI","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_8","alias_value":"WKLD5PY5","created_at":"2026-06-09T01:05:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WKLD5PY5XGD6IIEI2SZC4WKPPP","target":"record","payload":{"canonical_record":{"source":{"id":"2411.11350","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-18T07:39:46Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"1ea227d39a83e4f71ca3b618d9a0e2a3f8f8d44ff45cb6f1509d72b228ac12e7","abstract_canon_sha256":"aebc4feb705ddaa5eb040ddb16673f5d0d9b120a86b265298826f6865df2dd82"},"schema_version":"1.0"},"canonical_sha256":"b2963ebf1db987e42088d4b22e594f7bde4ea87727e3b246daee42c5c65733d3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:04.187220Z","signature_b64":"F6Jxkawuv82JySLd4xrxGV/hhCImeeZl+ey3N/GjAq1hcx6klNW66fnCTl0wTLic9Tpqa8bBlGd5HGAwbDxCBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2963ebf1db987e42088d4b22e594f7bde4ea87727e3b246daee42c5c65733d3","last_reissued_at":"2026-06-09T01:05:04.186710Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:04.186710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.11350","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AKsCntfqZ9kO79ToUkKQuA5ciVfZV4vvclRZfnu6cCtPT9Ikqj3TaeEAZ/U6gHN9ZtkhxaiZ5t7yvVtLaMoSBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T12:02:53.354050Z"},"content_sha256":"db7fe46c03e399acc7c5dcf4df8c0726ce2b366798ad972f957144ec1449727c","schema_version":"1.0","event_id":"sha256:db7fe46c03e399acc7c5dcf4df8c0726ce2b366798ad972f957144ec1449727c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WKLD5PY5XGD6IIEI2SZC4WKPPP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Zero and Few Shot Load Forecasting with Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Chengrui Zhang, Christian Rehtanz, Fernando Port\\'e-Agel, Jiannong Fang, Mengshuo Jia, Wenlong Liao, Zhe Yang","submitted_at":"2024-11-18T07:39:46Z","abstract_excerpt":"Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted as the Chronos model. By utilizing its extensive pre-trained knowledge, the Chronos model enables accurate load forecasting in data-scarce scenarios. Simula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11350","kind":"arxiv","version":2},"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/2411.11350/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":"2026-06-09T01:05:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LKdIbPrRb4j3VdaFTjVxBiIhYfMiMkJcPJ5XEn9MiMoyf7urkl7A9/T/JOLOtqV/a0+HOxYznC8EFGuQdxApBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T12:02:53.354437Z"},"content_sha256":"f231723a798a130494ba92a334df244151e00eea9f96a2caa7657e67ce10ecc6","schema_version":"1.0","event_id":"sha256:f231723a798a130494ba92a334df244151e00eea9f96a2caa7657e67ce10ecc6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/bundle.json","state_url":"https://pith.science/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/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":"2026-06-22T12:02:53Z","links":{"resolver":"https://pith.science/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP","bundle":"https://pith.science/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/bundle.json","state":"https://pith.science/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WKLD5PY5XGD6IIEI2SZC4WKPPP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WKLD5PY5XGD6IIEI2SZC4WKPPP","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":"aebc4feb705ddaa5eb040ddb16673f5d0d9b120a86b265298826f6865df2dd82","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-18T07:39:46Z","title_canon_sha256":"1ea227d39a83e4f71ca3b618d9a0e2a3f8f8d44ff45cb6f1509d72b228ac12e7"},"schema_version":"1.0","source":{"id":"2411.11350","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.11350","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"arxiv_version","alias_value":"2411.11350v2","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11350","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_12","alias_value":"WKLD5PY5XGD6","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_16","alias_value":"WKLD5PY5XGD6IIEI","created_at":"2026-06-09T01:05:04Z"},{"alias_kind":"pith_short_8","alias_value":"WKLD5PY5","created_at":"2026-06-09T01:05:04Z"}],"graph_snapshots":[{"event_id":"sha256:f231723a798a130494ba92a334df244151e00eea9f96a2caa7657e67ce10ecc6","target":"graph","created_at":"2026-06-09T01:05:04Z","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/2411.11350/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted as the Chronos model. By utilizing its extensive pre-trained knowledge, the Chronos model enables accurate load forecasting in data-scarce scenarios. Simula","authors_text":"Chengrui Zhang, Christian Rehtanz, Fernando Port\\'e-Agel, Jiannong Fang, Mengshuo Jia, Wenlong Liao, Zhe Yang","cross_cats":["eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-18T07:39:46Z","title":"Zero and Few Shot Load Forecasting with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11350","kind":"arxiv","version":2},"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:db7fe46c03e399acc7c5dcf4df8c0726ce2b366798ad972f957144ec1449727c","target":"record","created_at":"2026-06-09T01:05:04Z","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":"aebc4feb705ddaa5eb040ddb16673f5d0d9b120a86b265298826f6865df2dd82","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-18T07:39:46Z","title_canon_sha256":"1ea227d39a83e4f71ca3b618d9a0e2a3f8f8d44ff45cb6f1509d72b228ac12e7"},"schema_version":"1.0","source":{"id":"2411.11350","kind":"arxiv","version":2}},"canonical_sha256":"b2963ebf1db987e42088d4b22e594f7bde4ea87727e3b246daee42c5c65733d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2963ebf1db987e42088d4b22e594f7bde4ea87727e3b246daee42c5c65733d3","first_computed_at":"2026-06-09T01:05:04.186710Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:04.186710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F6Jxkawuv82JySLd4xrxGV/hhCImeeZl+ey3N/GjAq1hcx6klNW66fnCTl0wTLic9Tpqa8bBlGd5HGAwbDxCBg==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:04.187220Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.11350","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db7fe46c03e399acc7c5dcf4df8c0726ce2b366798ad972f957144ec1449727c","sha256:f231723a798a130494ba92a334df244151e00eea9f96a2caa7657e67ce10ecc6"],"state_sha256":"1a100fb913f9e9ed071998ea6cacf4aa39cb2a81e631ce32aea1e633929daa7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yGFdNDjcjO1JHh9yHVtmd7EGdnDVq0YkzFWlep0AHE7k38CPeEUS+EIITV7hVq9yvrHh+0qv5EV0q46rfKDzDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T12:02:53.356445Z","bundle_sha256":"29350d0187947bd827612b2cae2123b15347c604dd9af5f951f72a0892fb19c0"}}