{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VQEJVCMM2C6TEK27NB3UDKRU2P","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":"a8384f7db453b3f3a2fc09953c79e12bf974b127b3a54fff69f8922558792c88","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T08:40:13Z","title_canon_sha256":"e686cda11cfdef52813e7bc16e29a4fbb0e302adb760f18b3fa98721e9bf6990"},"schema_version":"1.0","source":{"id":"2509.04921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.04921","created_at":"2026-07-05T12:05:32Z"},{"alias_kind":"arxiv_version","alias_value":"2509.04921v1","created_at":"2026-07-05T12:05:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04921","created_at":"2026-07-05T12:05:32Z"},{"alias_kind":"pith_short_12","alias_value":"VQEJVCMM2C6T","created_at":"2026-07-05T12:05:32Z"},{"alias_kind":"pith_short_16","alias_value":"VQEJVCMM2C6TEK27","created_at":"2026-07-05T12:05:32Z"},{"alias_kind":"pith_short_8","alias_value":"VQEJVCMM","created_at":"2026-07-05T12:05:32Z"}],"graph_snapshots":[{"event_id":"sha256:b8da9a4ffa81c6432e35ef9e30a90ff4cadd62fff1ea72a898127050be85fc6b","target":"graph","created_at":"2026-07-05T12:05:32Z","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/2509.04921/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting plays a critical role in decision-making processes across diverse fields including meteorology, traffic, electricity, economics, finance, and so on. Especially, predicting returns on financial instruments is a challenging problem. Some researchers have proposed time series foundation models applicable to various forecasting tasks. Simultaneously, based on the recognition that real-world time series exhibit chaotic properties, methods have been developed to artificially generate synthetic chaotic time series, construct diverse datasets and train models. In this study, we","authors_text":"Yuki Takemoto","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T08:40:13Z","title":"Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04921","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":"sha256:1806abaea8061188ffcc4ba3843b27fcfb79eed5dcb52c335473256246492024","target":"record","created_at":"2026-07-05T12:05:32Z","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":"a8384f7db453b3f3a2fc09953c79e12bf974b127b3a54fff69f8922558792c88","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-05T08:40:13Z","title_canon_sha256":"e686cda11cfdef52813e7bc16e29a4fbb0e302adb760f18b3fa98721e9bf6990"},"schema_version":"1.0","source":{"id":"2509.04921","kind":"arxiv","version":1}},"canonical_sha256":"ac089a898cd0bd322b5f687741aa34d3e0e114c292afd3f1fce372bdc0cd8da9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac089a898cd0bd322b5f687741aa34d3e0e114c292afd3f1fce372bdc0cd8da9","first_computed_at":"2026-07-05T12:05:32.246567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:32.246567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uND2fvn5tbPZlyRwyJR9LRSxLZ5dVlrczrLIKG10hJb9uXyI7s3eInScTT8YfuMFLmlAgAIKKu+c1KKAkJ8oDw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:32.247002Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.04921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1806abaea8061188ffcc4ba3843b27fcfb79eed5dcb52c335473256246492024","sha256:b8da9a4ffa81c6432e35ef9e30a90ff4cadd62fff1ea72a898127050be85fc6b"],"state_sha256":"ee0f9862367cbd261a4d3e1e876a3394ce682fd43cc1104e73f66c2e7333ba4f"}