{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FXYRFKYW33KUOZJF5IG7IH73EV","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":"f8a588a48a2325a80e2d9a500611d6febbb59a8ad1bdedf3818e92e79352e9ab","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2025-02-25T21:06:31Z","title_canon_sha256":"b78be08a070223069f4c40569bbbbe4bf024e1b20ab396f938aba5867f170849"},"schema_version":"1.0","source":{"id":"2502.18645","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.18645","created_at":"2026-05-26T02:03:48Z"},{"alias_kind":"arxiv_version","alias_value":"2502.18645v1","created_at":"2026-05-26T02:03:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.18645","created_at":"2026-05-26T02:03:48Z"},{"alias_kind":"pith_short_12","alias_value":"FXYRFKYW33KU","created_at":"2026-05-26T02:03:48Z"},{"alias_kind":"pith_short_16","alias_value":"FXYRFKYW33KUOZJF","created_at":"2026-05-26T02:03:48Z"},{"alias_kind":"pith_short_8","alias_value":"FXYRFKYW","created_at":"2026-05-26T02:03:48Z"}],"graph_snapshots":[{"event_id":"sha256:e43ba68a81129ea0b2defed958c0d2b7027dc9be30d6ddebd8d260451a7a62b7","target":"graph","created_at":"2026-05-26T02:03:48Z","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/2502.18645/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series in natural sciences, such as hydrology and climatology, and other environmental applications, often consist of continuous observations constrained to the unit interval (0,1). Traditional Gaussian-based models fail to capture these bounds, requiring more flexible approaches. This paper introduces the Matsuoka Autoregressive Moving Average (MARMA) model, extending the GARMA framework by assuming a Matsuoka-distributed random component taking values in (0,1) and an ARMA-like systematic structure allowing for random time-dependent covariates. Parameter estimation is performed via parti","authors_text":"Bruna Gregory Palm, Danilo Hiroshi Matsuoka, Guilherme Pumi, Taiane Schaedler Prass","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2025-02-25T21:06:31Z","title":"A Matsuoka-Based GARMA Model for Hydrological Forecasting: Theory, Estimation, and Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18645","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":"sha256:96094b44bb2b534e8e11714487eefbcdcfea6afffde0d5ddd04f1277807dcfcd","target":"record","created_at":"2026-05-26T02:03:48Z","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":"f8a588a48a2325a80e2d9a500611d6febbb59a8ad1bdedf3818e92e79352e9ab","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2025-02-25T21:06:31Z","title_canon_sha256":"b78be08a070223069f4c40569bbbbe4bf024e1b20ab396f938aba5867f170849"},"schema_version":"1.0","source":{"id":"2502.18645","kind":"arxiv","version":1}},"canonical_sha256":"2df112ab16ded5476525ea0df41ffb256ed88da3de7ebb9de3d8377524d5b6fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2df112ab16ded5476525ea0df41ffb256ed88da3de7ebb9de3d8377524d5b6fc","first_computed_at":"2026-05-26T02:03:48.753749Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:03:48.753749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CFe7+VsZolltTdyTlOTLMf4wyF2ImRpZccT4kskgkDfpPnUDGZJNT5xto7wy/t5AbV+jMqRPlmUKpyvPnES6AQ==","signature_status":"signed_v1","signed_at":"2026-05-26T02:03:48.754786Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.18645","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96094b44bb2b534e8e11714487eefbcdcfea6afffde0d5ddd04f1277807dcfcd","sha256:e43ba68a81129ea0b2defed958c0d2b7027dc9be30d6ddebd8d260451a7a62b7"],"state_sha256":"6914054430250aa89a9307303ca2717dde79baae75509a5c5937e69c971ad806"}