{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:Q6JNYTXJWKGFUR2ZRBG5J6EPA2","short_pith_number":"pith:Q6JNYTXJ","canonical_record":{"source":{"id":"2109.12218","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T22:11:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0813670e5a1a4bd5f7824f5b88371fc15e695a389cdc74199b743b8510f502b5","abstract_canon_sha256":"36422e63b967cfedc2ab17f64f60b7d9536ce8007621f31f7d3874343d0cd1a0"},"schema_version":"1.0"},"canonical_sha256":"8792dc4ee9b28c5a4759884dd4f88f06a4453bb5b840940d68a1944e7fdbc86a","source":{"kind":"arxiv","id":"2109.12218","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.12218","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"arxiv_version","alias_value":"2109.12218v3","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.12218","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_12","alias_value":"Q6JNYTXJWKGF","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_16","alias_value":"Q6JNYTXJWKGFUR2Z","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_8","alias_value":"Q6JNYTXJ","created_at":"2026-07-05T05:52:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:Q6JNYTXJWKGFUR2ZRBG5J6EPA2","target":"record","payload":{"canonical_record":{"source":{"id":"2109.12218","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T22:11:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0813670e5a1a4bd5f7824f5b88371fc15e695a389cdc74199b743b8510f502b5","abstract_canon_sha256":"36422e63b967cfedc2ab17f64f60b7d9536ce8007621f31f7d3874343d0cd1a0"},"schema_version":"1.0"},"canonical_sha256":"8792dc4ee9b28c5a4759884dd4f88f06a4453bb5b840940d68a1944e7fdbc86a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:17.184079Z","signature_b64":"svSWnKM6KaESIeveowLPPi39O6hrjZo5qrcKtm5d+Z3tspc72eTzHRl4V/jGS3ZTjqSSRsuMgCWB3rP1GMddAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8792dc4ee9b28c5a4759884dd4f88f06a4453bb5b840940d68a1944e7fdbc86a","last_reissued_at":"2026-07-05T05:52:17.183684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:17.183684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.12218","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:52:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lk0TTWwIevCY5hafxSJy/xdnTvDSfgQ5EFYCsT9asFShRgL3naFnmWrHqdk9L8ApaTmAHkmQuCuZg8dNs+sdBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T09:53:02.341138Z"},"content_sha256":"e0fb31758f4fdb87db45f45e40bfd50053dcd9bb432b99cf27dd7e71641f008f","schema_version":"1.0","event_id":"sha256:e0fb31758f4fdb87db45f45e40bfd50053dcd9bb432b99cf27dd7e71641f008f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:Q6JNYTXJWKGFUR2ZRBG5J6EPA2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Long-Range Transformers for Dynamic Spatiotemporal Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jake Grigsby, Nam Nguyen, Yanjun Qi, Zhe Wang","submitted_at":"2021-09-24T22:11:46Z","abstract_excerpt":"Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables. In contrast, methods based on graph neural networks explicitly model variable relationships. However, these methods often rely on predefined graphs that cannot change over time and perform separate spatial and temporal updates without establishing direct connections between each variable at every time"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.12218","kind":"arxiv","version":3},"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/2109.12218/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":"2026-07-05T05:52:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JfM1Fvzri+4Wa2Hr3zoMvetpQGoafO/3BW2koniLTJVEKPHUsAsuhvFdhQZAEcXTlXrBor4jYuwt7xbtR8TMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T09:53:02.341733Z"},"content_sha256":"87e81c5e9ad5f3cf2e469cc9259e3a96c6d12164e8b61d691a975c6206304dff","schema_version":"1.0","event_id":"sha256:87e81c5e9ad5f3cf2e469cc9259e3a96c6d12164e8b61d691a975c6206304dff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/bundle.json","state_url":"https://pith.science/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/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":"2026-08-18T09:53:02Z","links":{"resolver":"https://pith.science/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2","bundle":"https://pith.science/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/bundle.json","state":"https://pith.science/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q6JNYTXJWKGFUR2ZRBG5J6EPA2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:Q6JNYTXJWKGFUR2ZRBG5J6EPA2","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":"36422e63b967cfedc2ab17f64f60b7d9536ce8007621f31f7d3874343d0cd1a0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T22:11:46Z","title_canon_sha256":"0813670e5a1a4bd5f7824f5b88371fc15e695a389cdc74199b743b8510f502b5"},"schema_version":"1.0","source":{"id":"2109.12218","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.12218","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"arxiv_version","alias_value":"2109.12218v3","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.12218","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_12","alias_value":"Q6JNYTXJWKGF","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_16","alias_value":"Q6JNYTXJWKGFUR2Z","created_at":"2026-07-05T05:52:17Z"},{"alias_kind":"pith_short_8","alias_value":"Q6JNYTXJ","created_at":"2026-07-05T05:52:17Z"}],"graph_snapshots":[{"event_id":"sha256:87e81c5e9ad5f3cf2e469cc9259e3a96c6d12164e8b61d691a975c6206304dff","target":"graph","created_at":"2026-07-05T05:52:17Z","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/2109.12218/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables. In contrast, methods based on graph neural networks explicitly model variable relationships. However, these methods often rely on predefined graphs that cannot change over time and perform separate spatial and temporal updates without establishing direct connections between each variable at every time","authors_text":"Jake Grigsby, Nam Nguyen, Yanjun Qi, Zhe Wang","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T22:11:46Z","title":"Long-Range Transformers for Dynamic Spatiotemporal Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.12218","kind":"arxiv","version":3},"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:e0fb31758f4fdb87db45f45e40bfd50053dcd9bb432b99cf27dd7e71641f008f","target":"record","created_at":"2026-07-05T05:52:17Z","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":"36422e63b967cfedc2ab17f64f60b7d9536ce8007621f31f7d3874343d0cd1a0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T22:11:46Z","title_canon_sha256":"0813670e5a1a4bd5f7824f5b88371fc15e695a389cdc74199b743b8510f502b5"},"schema_version":"1.0","source":{"id":"2109.12218","kind":"arxiv","version":3}},"canonical_sha256":"8792dc4ee9b28c5a4759884dd4f88f06a4453bb5b840940d68a1944e7fdbc86a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8792dc4ee9b28c5a4759884dd4f88f06a4453bb5b840940d68a1944e7fdbc86a","first_computed_at":"2026-07-05T05:52:17.183684Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:52:17.183684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"svSWnKM6KaESIeveowLPPi39O6hrjZo5qrcKtm5d+Z3tspc72eTzHRl4V/jGS3ZTjqSSRsuMgCWB3rP1GMddAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:52:17.184079Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.12218","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0fb31758f4fdb87db45f45e40bfd50053dcd9bb432b99cf27dd7e71641f008f","sha256:87e81c5e9ad5f3cf2e469cc9259e3a96c6d12164e8b61d691a975c6206304dff"],"state_sha256":"ac4e07bb1b92b3f485e2ee7df4a01ae17f1ec89b84595f69417599c1bdda0a64"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VLHuq6FI1Tnqrr21jRYPTPiz2iEMVrPPLAfENkq/jvG0EBS8qlJOSciYZKGeJjrvT/GH6bANDHyn44ByF/qgCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T09:53:02.347144Z","bundle_sha256":"b1abd2dbf7cc00b8d84040a89b483598477bcc311f7519bdb9c1dcaa7f7bb4e2"}}