{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LNMR77D7FCPPQH3MQGYYAPXW23","short_pith_number":"pith:LNMR77D7","canonical_record":{"source":{"id":"2505.13521","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T13:27:39Z","cross_cats_sorted":["q-fin.RM","stat.AP"],"title_canon_sha256":"2b603322b1f492112d685fbcf98498cca28803f1ce71c231e5b9bddbafecdb98","abstract_canon_sha256":"50c9bf31c1977741d05d57548322a775709a4516fdddb03c5f5ee2e8cc097ef8"},"schema_version":"1.0"},"canonical_sha256":"5b591ffc7f289ef81f6c81b1803ef6d6f877a48b277d8c979f7bd38a15b9062c","source":{"kind":"arxiv","id":"2505.13521","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13521","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13521v1","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13521","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_12","alias_value":"LNMR77D7FCPP","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_16","alias_value":"LNMR77D7FCPPQH3M","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_8","alias_value":"LNMR77D7","created_at":"2026-07-05T11:05:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LNMR77D7FCPPQH3MQGYYAPXW23","target":"record","payload":{"canonical_record":{"source":{"id":"2505.13521","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T13:27:39Z","cross_cats_sorted":["q-fin.RM","stat.AP"],"title_canon_sha256":"2b603322b1f492112d685fbcf98498cca28803f1ce71c231e5b9bddbafecdb98","abstract_canon_sha256":"50c9bf31c1977741d05d57548322a775709a4516fdddb03c5f5ee2e8cc097ef8"},"schema_version":"1.0"},"canonical_sha256":"5b591ffc7f289ef81f6c81b1803ef6d6f877a48b277d8c979f7bd38a15b9062c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:47.219924Z","signature_b64":"i6MH9CMwU3f0HXfQo9imvIgkrYRcjpnRXnvauWO9FfqKx//XV61UGk8laXVTenjx/7AbQqgZS8Oo/PuQXVMABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b591ffc7f289ef81f6c81b1803ef6d6f877a48b277d8c979f7bd38a15b9062c","last_reissued_at":"2026-07-05T11:05:47.219468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:47.219468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.13521","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":"2026-07-05T11:05:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IjyAQP1o6Egf6/ahhaYw56DlgWdtevS2iDJxfgvpuG+HLxIR5m33XKFQf298fduUwnlnzmU4szFwQ5ZJa3noDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T14:48:03.985271Z"},"content_sha256":"2ea2045b7029b29375d5a2d74397e7d9158a02dba44d084d4a4a3582efb2d1a2","schema_version":"1.0","event_id":"sha256:2ea2045b7029b29375d5a2d74397e7d9158a02dba44d084d4a4a3582efb2d1a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LNMR77D7FCPPQH3MQGYYAPXW23","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Zero-Shot Forecasting Mortality Rates: A Global Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.RM","stat.AP"],"primary_cat":"cs.LG","authors_text":"Bernadett Aradi, Gabor Petnehazi, Jozsef Gall, Laith Al Shaggah","submitted_at":"2025-05-17T13:27:39Z","abstract_excerpt":"This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-specific fine-tuning. We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries and 111 age groups. In our investigations, zero-shot models showed varying results: while CHRONOS delivered competitive shorter-term forecasts, outperforming traditional methods like AR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13521","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/2505.13521/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":"2026-07-05T11:05:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UWO7GvTIq6IytbFjAjozTYiRWKuk1MH7oVaUVA/BvUf6Jhjafc+r98omM6aE4k8Fuka0GSobPyeaIKVezlRJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T14:48:03.985647Z"},"content_sha256":"04e3e1127e379b52ee6bdfc28cc56daa010d9e2e729adcbf50a6987fc9f0b8f0","schema_version":"1.0","event_id":"sha256:04e3e1127e379b52ee6bdfc28cc56daa010d9e2e729adcbf50a6987fc9f0b8f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LNMR77D7FCPPQH3MQGYYAPXW23/bundle.json","state_url":"https://pith.science/pith/LNMR77D7FCPPQH3MQGYYAPXW23/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LNMR77D7FCPPQH3MQGYYAPXW23/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":"2026-08-19T14:48:03Z","links":{"resolver":"https://pith.science/pith/LNMR77D7FCPPQH3MQGYYAPXW23","bundle":"https://pith.science/pith/LNMR77D7FCPPQH3MQGYYAPXW23/bundle.json","state":"https://pith.science/pith/LNMR77D7FCPPQH3MQGYYAPXW23/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LNMR77D7FCPPQH3MQGYYAPXW23/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LNMR77D7FCPPQH3MQGYYAPXW23","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":"50c9bf31c1977741d05d57548322a775709a4516fdddb03c5f5ee2e8cc097ef8","cross_cats_sorted":["q-fin.RM","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T13:27:39Z","title_canon_sha256":"2b603322b1f492112d685fbcf98498cca28803f1ce71c231e5b9bddbafecdb98"},"schema_version":"1.0","source":{"id":"2505.13521","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13521","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13521v1","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13521","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_12","alias_value":"LNMR77D7FCPP","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_16","alias_value":"LNMR77D7FCPPQH3M","created_at":"2026-07-05T11:05:47Z"},{"alias_kind":"pith_short_8","alias_value":"LNMR77D7","created_at":"2026-07-05T11:05:47Z"}],"graph_snapshots":[{"event_id":"sha256:04e3e1127e379b52ee6bdfc28cc56daa010d9e2e729adcbf50a6987fc9f0b8f0","target":"graph","created_at":"2026-07-05T11:05:47Z","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/2505.13521/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-specific fine-tuning. We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries and 111 age groups. In our investigations, zero-shot models showed varying results: while CHRONOS delivered competitive shorter-term forecasts, outperforming traditional methods like AR","authors_text":"Bernadett Aradi, Gabor Petnehazi, Jozsef Gall, Laith Al Shaggah","cross_cats":["q-fin.RM","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T13:27:39Z","title":"Zero-Shot Forecasting Mortality Rates: A Global Study"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13521","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":"sha256:2ea2045b7029b29375d5a2d74397e7d9158a02dba44d084d4a4a3582efb2d1a2","target":"record","created_at":"2026-07-05T11:05:47Z","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":"50c9bf31c1977741d05d57548322a775709a4516fdddb03c5f5ee2e8cc097ef8","cross_cats_sorted":["q-fin.RM","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-17T13:27:39Z","title_canon_sha256":"2b603322b1f492112d685fbcf98498cca28803f1ce71c231e5b9bddbafecdb98"},"schema_version":"1.0","source":{"id":"2505.13521","kind":"arxiv","version":1}},"canonical_sha256":"5b591ffc7f289ef81f6c81b1803ef6d6f877a48b277d8c979f7bd38a15b9062c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b591ffc7f289ef81f6c81b1803ef6d6f877a48b277d8c979f7bd38a15b9062c","first_computed_at":"2026-07-05T11:05:47.219468Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:47.219468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i6MH9CMwU3f0HXfQo9imvIgkrYRcjpnRXnvauWO9FfqKx//XV61UGk8laXVTenjx/7AbQqgZS8Oo/PuQXVMABw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:47.219924Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.13521","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ea2045b7029b29375d5a2d74397e7d9158a02dba44d084d4a4a3582efb2d1a2","sha256:04e3e1127e379b52ee6bdfc28cc56daa010d9e2e729adcbf50a6987fc9f0b8f0"],"state_sha256":"588d6da96774dcc2bc86bc2813803532f23434021de563701bc0a1aecdb2ab17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8fCtNqGAvsi6QMehwmHxmtEVRsfp/KOyuoWuNkpwJnl36oyIJ75Jugvp+qSVbr3OoJMcUC12GNYX9cxhvNOVCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T14:48:03.988614Z","bundle_sha256":"4ab2beae3ef7961790d014bc55eb1e9d86c4ebe1a52a14995db4b76b2c98273b"}}