{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PALJ7VRZ5J3UHYQYYANCVAW3CZ","short_pith_number":"pith:PALJ7VRZ","canonical_record":{"source":{"id":"2309.10186","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-18T22:25:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"59ff067d19ce287e5e191228887325eacaeac23b990896ec859d3d1138ebe6a3","abstract_canon_sha256":"1cc25ff4f2cdf6203d042fc6b36a8f832c224700d1739565d9d19a8237c3ec58"},"schema_version":"1.0"},"canonical_sha256":"78169fd639ea7743e218c01a2a82db16742075a50dcac0daf0ef340ddfc949f8","source":{"kind":"arxiv","id":"2309.10186","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10186","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10186v2","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10186","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_12","alias_value":"PALJ7VRZ5J3U","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_16","alias_value":"PALJ7VRZ5J3UHYQY","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_8","alias_value":"PALJ7VRZ","created_at":"2026-07-05T11:21:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PALJ7VRZ5J3UHYQYYANCVAW3CZ","target":"record","payload":{"canonical_record":{"source":{"id":"2309.10186","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-18T22:25:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"59ff067d19ce287e5e191228887325eacaeac23b990896ec859d3d1138ebe6a3","abstract_canon_sha256":"1cc25ff4f2cdf6203d042fc6b36a8f832c224700d1739565d9d19a8237c3ec58"},"schema_version":"1.0"},"canonical_sha256":"78169fd639ea7743e218c01a2a82db16742075a50dcac0daf0ef340ddfc949f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:29.940393Z","signature_b64":"N0yV+SXU4qJf9sctzpGQ8nTzTlcCCqLrKwWdAQdCdCQE9PyNLDctDVU2ovCfd5mKBjsx08O3NHkoqvFoK0L7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78169fd639ea7743e218c01a2a82db16742075a50dcac0daf0ef340ddfc949f8","last_reissued_at":"2026-07-05T11:21:29.939885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:29.939885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.10186","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":"2026-07-05T11:21:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q5vtn4Z9tMm0eoyYQUzYxp9m61J3yu2whshcr7JiC+VRVByH/WMNKuyLUgxM1ux2s2scTwH8HCj00noQOYmPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T01:20:40.751905Z"},"content_sha256":"549ec98808455c664f03a114969c9992d5bf7af1ef10c9d4d3f9beb0d4106913","schema_version":"1.0","event_id":"sha256:549ec98808455c664f03a114969c9992d5bf7af1ef10c9d4d3f9beb0d4106913"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PALJ7VRZ5J3UHYQYYANCVAW3CZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoran Xie, Jianming Yong, Lin Li, Thanveer Shaik, Xiaohui Tao, Yuefeng Li","submitted_at":"2023-09-18T22:25:12Z","abstract_excerpt":"Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks. However, deep learning has its limitations such as the assumption of equally spaced and ordered data, and the lack of ability to incorporate graph structure in terms of time-series prediction. Graphical neural network (GNN) has the ability to overcome these challenges and capture the temporal dependencies in time-series data. In this study, we propose a novel approach for pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10186","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/2309.10186/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":"2026-07-05T11:21:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GrUWchhJOk2LMKbTn/MKXesOj5UgBl8kgRARNxBosX1zLw55MPOYtIO8nPdMT52VtLtQUrag8VJ7xu+42Z9MCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T01:20:40.752500Z"},"content_sha256":"28e6a152d4f6c38e890a021f73bc87f22c93906e70ad3da021ea026923a4f204","schema_version":"1.0","event_id":"sha256:28e6a152d4f6c38e890a021f73bc87f22c93906e70ad3da021ea026923a4f204"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/bundle.json","state_url":"https://pith.science/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/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":"2026-08-13T01:20:40Z","links":{"resolver":"https://pith.science/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ","bundle":"https://pith.science/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/bundle.json","state":"https://pith.science/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PALJ7VRZ5J3UHYQYYANCVAW3CZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PALJ7VRZ5J3UHYQYYANCVAW3CZ","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":"1cc25ff4f2cdf6203d042fc6b36a8f832c224700d1739565d9d19a8237c3ec58","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-18T22:25:12Z","title_canon_sha256":"59ff067d19ce287e5e191228887325eacaeac23b990896ec859d3d1138ebe6a3"},"schema_version":"1.0","source":{"id":"2309.10186","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10186","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10186v2","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10186","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_12","alias_value":"PALJ7VRZ5J3U","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_16","alias_value":"PALJ7VRZ5J3UHYQY","created_at":"2026-07-05T11:21:29Z"},{"alias_kind":"pith_short_8","alias_value":"PALJ7VRZ","created_at":"2026-07-05T11:21:29Z"}],"graph_snapshots":[{"event_id":"sha256:28e6a152d4f6c38e890a021f73bc87f22c93906e70ad3da021ea026923a4f204","target":"graph","created_at":"2026-07-05T11:21:29Z","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/2309.10186/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks. However, deep learning has its limitations such as the assumption of equally spaced and ordered data, and the lack of ability to incorporate graph structure in terms of time-series prediction. Graphical neural network (GNN) has the ability to overcome these challenges and capture the temporal dependencies in time-series data. In this study, we propose a novel approach for pred","authors_text":"Haoran Xie, Jianming Yong, Lin Li, Thanveer Shaik, Xiaohui Tao, Yuefeng Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-18T22:25:12Z","title":"Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10186","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":"sha256:549ec98808455c664f03a114969c9992d5bf7af1ef10c9d4d3f9beb0d4106913","target":"record","created_at":"2026-07-05T11:21:29Z","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":"1cc25ff4f2cdf6203d042fc6b36a8f832c224700d1739565d9d19a8237c3ec58","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-18T22:25:12Z","title_canon_sha256":"59ff067d19ce287e5e191228887325eacaeac23b990896ec859d3d1138ebe6a3"},"schema_version":"1.0","source":{"id":"2309.10186","kind":"arxiv","version":2}},"canonical_sha256":"78169fd639ea7743e218c01a2a82db16742075a50dcac0daf0ef340ddfc949f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78169fd639ea7743e218c01a2a82db16742075a50dcac0daf0ef340ddfc949f8","first_computed_at":"2026-07-05T11:21:29.939885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:29.939885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N0yV+SXU4qJf9sctzpGQ8nTzTlcCCqLrKwWdAQdCdCQE9PyNLDctDVU2ovCfd5mKBjsx08O3NHkoqvFoK0L7AA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:29.940393Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.10186","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:549ec98808455c664f03a114969c9992d5bf7af1ef10c9d4d3f9beb0d4106913","sha256:28e6a152d4f6c38e890a021f73bc87f22c93906e70ad3da021ea026923a4f204"],"state_sha256":"74ec759d0ffb981b93f26be2240e4cad1389c927eecb6c0db136171af2724128"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cd2iBus6OxQNXdBUGN5kbYVyT+/QrBg47yx4m8sb+5rV+OThGKyqDuXGKpOwaFxSKgQ0vhbKtypn8oxpS6k2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T01:20:40.756656Z","bundle_sha256":"80fca3921359cf5bbcf47e01957d4de7d8949c119f62179ba6de7e936fe072f7"}}