{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SIELYKNZ23BD765EXFKQURMR3V","short_pith_number":"pith:SIELYKNZ","schema_version":"1.0","canonical_sha256":"9208bc29b9d6c23ffba4b9550a4591dd5caa0227c620a9b1c5e33dd6c0f5f69e","source":{"kind":"arxiv","id":"2503.04386","version":1},"attestation_state":"computed","paper":{"title":"Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Brooks Paige, Jim Griffin, Yiyong Luo","submitted_at":"2025-03-06T12:37:55Z","abstract_excerpt":"Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in identifiability, interpretability, and integration with traditional nonlinear time series methods. We address these challenges through two contributions. First, we introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso"},"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":"2503.04386","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T12:37:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"51fcebf3632e82cea97bf9320a9687cc48fa68fbfae404cd506e9ae41a24b6e3","abstract_canon_sha256":"6e312ef8c4edc960751973f97871773305cd437ca550ea544df5efe073910c4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:37.621223Z","signature_b64":"81YBblv0YtTTIAUEHcYw0MG/CavCudOnp/cTct1ans005Bb6zW713isiODXDkoFPn4WE7mTpz8rvmAIfzMwHCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9208bc29b9d6c23ffba4b9550a4591dd5caa0227c620a9b1c5e33dd6c0f5f69e","last_reissued_at":"2026-07-05T10:25:37.620508Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:37.620508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Brooks Paige, Jim Griffin, Yiyong Luo","submitted_at":"2025-03-06T12:37:55Z","abstract_excerpt":"Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in identifiability, interpretability, and integration with traditional nonlinear time series methods. We address these challenges through two contributions. First, we introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04386","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/2503.04386/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.04386","created_at":"2026-07-05T10:25:37.620601+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.04386v1","created_at":"2026-07-05T10:25:37.620601+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04386","created_at":"2026-07-05T10:25:37.620601+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIELYKNZ23BD","created_at":"2026-07-05T10:25:37.620601+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIELYKNZ23BD765E","created_at":"2026-07-05T10:25:37.620601+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIELYKNZ","created_at":"2026-07-05T10:25:37.620601+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04928","citing_title":"A Bayesian Gaussian Process Dynamic Factor Model","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V","json":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V.json","graph_json":"https://pith.science/api/pith-number/SIELYKNZ23BD765EXFKQURMR3V/graph.json","events_json":"https://pith.science/api/pith-number/SIELYKNZ23BD765EXFKQURMR3V/events.json","paper":"https://pith.science/paper/SIELYKNZ"},"agent_actions":{"view_html":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V","download_json":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V.json","view_paper":"https://pith.science/paper/SIELYKNZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.04386&json=true","fetch_graph":"https://pith.science/api/pith-number/SIELYKNZ23BD765EXFKQURMR3V/graph.json","fetch_events":"https://pith.science/api/pith-number/SIELYKNZ23BD765EXFKQURMR3V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V/action/storage_attestation","attest_author":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V/action/author_attestation","sign_citation":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V/action/citation_signature","submit_replication":"https://pith.science/pith/SIELYKNZ23BD765EXFKQURMR3V/action/replication_record"}},"created_at":"2026-07-05T10:25:37.620601+00:00","updated_at":"2026-07-05T10:25:37.620601+00:00"}