{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CYPFAXVMXVBNIJHC7Y3K2GJPNV","short_pith_number":"pith:CYPFAXVM","canonical_record":{"source":{"id":"2506.01403","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T07:54:03Z","cross_cats_sorted":[],"title_canon_sha256":"7295aa63d66e95bf1d87b013f31edeaf0a061073bc77c63c9969f7f9d1750b1f","abstract_canon_sha256":"66a272a925e808649d0b3164e756f5ec3c93cb6a14e98852869586d22adad347"},"schema_version":"1.0"},"canonical_sha256":"161e505eacbd42d424e2fe36ad192f6d7b5b9c3919771d34b300f7ee199d25a1","source":{"kind":"arxiv","id":"2506.01403","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01403","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01403v1","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01403","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_12","alias_value":"CYPFAXVMXVBN","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_16","alias_value":"CYPFAXVMXVBNIJHC","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_8","alias_value":"CYPFAXVM","created_at":"2026-07-05T11:14:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CYPFAXVMXVBNIJHC7Y3K2GJPNV","target":"record","payload":{"canonical_record":{"source":{"id":"2506.01403","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T07:54:03Z","cross_cats_sorted":[],"title_canon_sha256":"7295aa63d66e95bf1d87b013f31edeaf0a061073bc77c63c9969f7f9d1750b1f","abstract_canon_sha256":"66a272a925e808649d0b3164e756f5ec3c93cb6a14e98852869586d22adad347"},"schema_version":"1.0"},"canonical_sha256":"161e505eacbd42d424e2fe36ad192f6d7b5b9c3919771d34b300f7ee199d25a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:12.678139Z","signature_b64":"IgZy+n2BJJyHZ8VaVzGQi6yMHK7oFJ9T9kmS0bulJLPHyB9hpkVmL2gQk5EcHZesJVD6vPcxUowM5OSNSA+jBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"161e505eacbd42d424e2fe36ad192f6d7b5b9c3919771d34b300f7ee199d25a1","last_reissued_at":"2026-07-05T11:14:12.677675Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:12.677675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.01403","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":"2026-07-05T11:14:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MUwygdE/cgMiuQ8dScWVH+bU7bcGaR1EbeVPPx4NrrMUNlwKi0B3VwODTkRdRSVIiSZKvkm/x7iGCpZZZTvYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:22:02.937596Z"},"content_sha256":"60c33eef2df8bf64eb042c40ce62ea93fff0877be9805ce47c24f7cf47727c96","schema_version":"1.0","event_id":"sha256:60c33eef2df8bf64eb042c40ce62ea93fff0877be9805ce47c24f7cf47727c96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CYPFAXVMXVBNIJHC7Y3K2GJPNV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Dimensional Regularized Additive Matrix Autoregressive Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Debika Ghosh, Nilanjana Chakraborty, Samrat Roy","submitted_at":"2025-06-02T07:54:03Z","abstract_excerpt":"High-dimensional time series has diverse applications in econometrics and finance. Recent models for capturing temporal dependence have employed a bilinear representation for matrix time series, or the Tucker-decomposition based representation in case of tensor time series. A bilinear or Tucker-decomposition based temporal effect is difficult to interpret on many occasions, along with its computational complexity due to the non-convex nature of the underlying optimization problem. Moreover, the existing matrix case models have not sufficiently explored the possibilities of imposing any lower-d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01403","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/2506.01403/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":"2026-07-05T11:14:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jRT5QXqEgiupdwn1QnpUZYm5gB7r1lIM/WquFIoqrvclYevXSUwGyn/DMqOHgehzb28TK3I/49ylH2VOAahpDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:22:02.938486Z"},"content_sha256":"b3d6443a6035b16f0b927e518917bace0d8a7837d454c71e732e21c05ca19d9f","schema_version":"1.0","event_id":"sha256:b3d6443a6035b16f0b927e518917bace0d8a7837d454c71e732e21c05ca19d9f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/bundle.json","state_url":"https://pith.science/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/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":"2026-08-08T16:22:02Z","links":{"resolver":"https://pith.science/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV","bundle":"https://pith.science/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/bundle.json","state":"https://pith.science/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CYPFAXVMXVBNIJHC7Y3K2GJPNV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CYPFAXVMXVBNIJHC7Y3K2GJPNV","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":"66a272a925e808649d0b3164e756f5ec3c93cb6a14e98852869586d22adad347","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T07:54:03Z","title_canon_sha256":"7295aa63d66e95bf1d87b013f31edeaf0a061073bc77c63c9969f7f9d1750b1f"},"schema_version":"1.0","source":{"id":"2506.01403","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01403","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01403v1","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01403","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_12","alias_value":"CYPFAXVMXVBN","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_16","alias_value":"CYPFAXVMXVBNIJHC","created_at":"2026-07-05T11:14:12Z"},{"alias_kind":"pith_short_8","alias_value":"CYPFAXVM","created_at":"2026-07-05T11:14:12Z"}],"graph_snapshots":[{"event_id":"sha256:b3d6443a6035b16f0b927e518917bace0d8a7837d454c71e732e21c05ca19d9f","target":"graph","created_at":"2026-07-05T11:14:12Z","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/2506.01403/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional time series has diverse applications in econometrics and finance. Recent models for capturing temporal dependence have employed a bilinear representation for matrix time series, or the Tucker-decomposition based representation in case of tensor time series. A bilinear or Tucker-decomposition based temporal effect is difficult to interpret on many occasions, along with its computational complexity due to the non-convex nature of the underlying optimization problem. Moreover, the existing matrix case models have not sufficiently explored the possibilities of imposing any lower-d","authors_text":"Debika Ghosh, Nilanjana Chakraborty, Samrat Roy","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T07:54:03Z","title":"High-Dimensional Regularized Additive Matrix Autoregressive Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01403","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":"sha256:60c33eef2df8bf64eb042c40ce62ea93fff0877be9805ce47c24f7cf47727c96","target":"record","created_at":"2026-07-05T11:14:12Z","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":"66a272a925e808649d0b3164e756f5ec3c93cb6a14e98852869586d22adad347","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T07:54:03Z","title_canon_sha256":"7295aa63d66e95bf1d87b013f31edeaf0a061073bc77c63c9969f7f9d1750b1f"},"schema_version":"1.0","source":{"id":"2506.01403","kind":"arxiv","version":1}},"canonical_sha256":"161e505eacbd42d424e2fe36ad192f6d7b5b9c3919771d34b300f7ee199d25a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"161e505eacbd42d424e2fe36ad192f6d7b5b9c3919771d34b300f7ee199d25a1","first_computed_at":"2026-07-05T11:14:12.677675Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:12.677675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IgZy+n2BJJyHZ8VaVzGQi6yMHK7oFJ9T9kmS0bulJLPHyB9hpkVmL2gQk5EcHZesJVD6vPcxUowM5OSNSA+jBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:12.678139Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.01403","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60c33eef2df8bf64eb042c40ce62ea93fff0877be9805ce47c24f7cf47727c96","sha256:b3d6443a6035b16f0b927e518917bace0d8a7837d454c71e732e21c05ca19d9f"],"state_sha256":"0b629372cb2d240d05a80e4e2ba0948b2f3daa00da0b1b9a3996129df1b56d86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xfwdFChlV/CNBTdFHoNgJ4Ol4JzqsAPijmjsayjvDXWlLCojP8UZ7b8Kx+QoNuMQI4M3Ov0TlVzsOv/2uELOBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T16:22:02.945292Z","bundle_sha256":"efc9fa6d6f705d0eabe6aa1e19c066090fd8edb0e0499cc806edd83cb343efcc"}}