{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:PBTMCDKWF5Z6EMHOI5FD4FPEDN","short_pith_number":"pith:PBTMCDKW","schema_version":"1.0","canonical_sha256":"7866c10d562f73e230ee474a3e15e41b4222576811384e545679c81c2c1797e3","source":{"kind":"arxiv","id":"1106.1887","version":4},"attestation_state":"computed","paper":{"title":"Learning the Dependence Graph of Time Series with Latent Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Jalali, Sujay Sanghavi","submitted_at":"2011-06-09T19:34:29Z","abstract_excerpt":"This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent. In particular, we observe the time evolution of some variables, and never observe other variables; from this, we would like to find the dependency structure between the observed variables - separating out the spurious interactions caused by the (marginalizing out of the) latent variables' time series. We develop a new method, based on convex optimization, to do so in the case when the number of latent variables is s"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1106.1887","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-06-09T19:34:29Z","cross_cats_sorted":[],"title_canon_sha256":"934bf47c2eab1e9036c2b6e42d643311eda9564290c05efa6e63aabc1b50c950","abstract_canon_sha256":"741c6d6319efcf7eae17218d7d7f9a466e2219250b8061ede4ca8e2229c5e2d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:56:36.319054Z","signature_b64":"Jw+X4GlG1rtX2ZSb5JQQPpm7eKw/72VqOodyDmohaGOw54Fi2GtFGTYGnh1DUW1A2huQXjpTM8ZAb5dEmLWNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7866c10d562f73e230ee474a3e15e41b4222576811384e545679c81c2c1797e3","last_reissued_at":"2026-05-18T03:56:36.318616Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:56:36.318616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning the Dependence Graph of Time Series with Latent Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Jalali, Sujay Sanghavi","submitted_at":"2011-06-09T19:34:29Z","abstract_excerpt":"This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent. In particular, we observe the time evolution of some variables, and never observe other variables; from this, we would like to find the dependency structure between the observed variables - separating out the spurious interactions caused by the (marginalizing out of the) latent variables' time series. We develop a new method, based on convex optimization, to do so in the case when the number of latent variables is s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.1887","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1106.1887","created_at":"2026-05-18T03:56:36.318675+00:00"},{"alias_kind":"arxiv_version","alias_value":"1106.1887v4","created_at":"2026-05-18T03:56:36.318675+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1106.1887","created_at":"2026-05-18T03:56:36.318675+00:00"},{"alias_kind":"pith_short_12","alias_value":"PBTMCDKWF5Z6","created_at":"2026-05-18T12:26:39.201973+00:00"},{"alias_kind":"pith_short_16","alias_value":"PBTMCDKWF5Z6EMHO","created_at":"2026-05-18T12:26:39.201973+00:00"},{"alias_kind":"pith_short_8","alias_value":"PBTMCDKW","created_at":"2026-05-18T12:26:39.201973+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN","json":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN.json","graph_json":"https://pith.science/api/pith-number/PBTMCDKWF5Z6EMHOI5FD4FPEDN/graph.json","events_json":"https://pith.science/api/pith-number/PBTMCDKWF5Z6EMHOI5FD4FPEDN/events.json","paper":"https://pith.science/paper/PBTMCDKW"},"agent_actions":{"view_html":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN","download_json":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN.json","view_paper":"https://pith.science/paper/PBTMCDKW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1106.1887&json=true","fetch_graph":"https://pith.science/api/pith-number/PBTMCDKWF5Z6EMHOI5FD4FPEDN/graph.json","fetch_events":"https://pith.science/api/pith-number/PBTMCDKWF5Z6EMHOI5FD4FPEDN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN/action/storage_attestation","attest_author":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN/action/author_attestation","sign_citation":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN/action/citation_signature","submit_replication":"https://pith.science/pith/PBTMCDKWF5Z6EMHOI5FD4FPEDN/action/replication_record"}},"created_at":"2026-05-18T03:56:36.318675+00:00","updated_at":"2026-05-18T03:56:36.318675+00:00"}