{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:WB34EHJUR5IDA2EMVIHBHLYPDM","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":"f92b309758fb8b1f36bddb72b66214b0470cb61f193754215b030023068d0a30","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-06-08T22:02:06Z","title_canon_sha256":"f25d1a3698efca4b5e9e021ab2381f76e4c5110565ec7c1f4a31d69778f7df12"},"schema_version":"1.0","source":{"id":"1706.02781","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02781","created_at":"2026-05-18T00:42:40Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02781v1","created_at":"2026-05-18T00:42:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02781","created_at":"2026-05-18T00:42:40Z"},{"alias_kind":"pith_short_12","alias_value":"WB34EHJUR5ID","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WB34EHJUR5IDA2EM","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WB34EHJU","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:fa5e0e388253fc28ca7dd596a8ca5ce31f1f1857b37d680a4c4af951fdb1518f","target":"graph","created_at":"2026-05-18T00:42:40Z","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"},"paper":{"abstract_excerpt":"We present a new framework for learning Granger causality networks for multivariate categorical time series, based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of many local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The new formulation facilitates the application of MTD to high-dimensional multivariate time series. As a baseline, we also formulate a multi-output logistic autoregressive model (mLTD), which while a straightforward extension of autoregre","authors_text":"Alex Tank, Ali Shojaie, Emily B. Fox","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-06-08T22:02:06Z","title":"Granger Causality Networks for Categorical Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02781","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:c27b8d8bb3d4c7a906e8abcd6f9ad431a54f69aff41110ba9590041d07113556","target":"record","created_at":"2026-05-18T00:42:40Z","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":"f92b309758fb8b1f36bddb72b66214b0470cb61f193754215b030023068d0a30","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-06-08T22:02:06Z","title_canon_sha256":"f25d1a3698efca4b5e9e021ab2381f76e4c5110565ec7c1f4a31d69778f7df12"},"schema_version":"1.0","source":{"id":"1706.02781","kind":"arxiv","version":1}},"canonical_sha256":"b077c21d348f5030688caa0e13af0f1b3a89e2e82faf045af7d2932ba69de3b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b077c21d348f5030688caa0e13af0f1b3a89e2e82faf045af7d2932ba69de3b4","first_computed_at":"2026-05-18T00:42:40.940494Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:40.940494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gXCmXf7qmrK7VSNEK11r/hoS+ZrkUhCHZUkkXjzAgza0uVgVrDLbaoYtoHp4sgeWVpMc5c5Omg7lllukdgViDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:40.941171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02781","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c27b8d8bb3d4c7a906e8abcd6f9ad431a54f69aff41110ba9590041d07113556","sha256:fa5e0e388253fc28ca7dd596a8ca5ce31f1f1857b37d680a4c4af951fdb1518f"],"state_sha256":"983e32eb913346fe53739dc67a77535703e2df04abe21edf85b6cc6cd0bc594f"}