{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TPJDXITEKXGRCGJC7DNONRTJIC","short_pith_number":"pith:TPJDXITE","schema_version":"1.0","canonical_sha256":"9bd23ba26455cd111922f8dae6c66940837bd1bcf3fb98fe0bc53129c1dd5d1a","source":{"kind":"arxiv","id":"1901.06030","version":1},"attestation_state":"computed","paper":{"title":"A robust functional time series forecasting method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Han Lin Shang","submitted_at":"2019-01-17T23:16:34Z","abstract_excerpt":"Univariate time series often take the form of a collection of curves observed sequentially over time. Examples of these include hourly ground-level ozone concentration curves. These curves can be viewed as a time series of functions observed at equally spaced intervals over a dense grid. Since functional time series may contain various types of outliers, we introduce a robust functional time series forecasting method to down-weigh the influence of outliers in forecasting. Through a robust principal component analysis based on projection pursuit, a time series of functions can be decomposed int"},"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":"1901.06030","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-01-17T23:16:34Z","cross_cats_sorted":[],"title_canon_sha256":"cd0707d8fad0519f8c414d5bf5daa8ca2124a6a1dfd9517a5ca1b61723b3687d","abstract_canon_sha256":"6b7c0c7b406afbff6e5e3108d17e965cc2d093ba09ad1897a71cf08487b9c489"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:45.806379Z","signature_b64":"8gTLagkQzsVMfT9JHQe7Y2Js5AfgRMlNpP5No4XkOjW0OI+WzzIvQuvQPxNKf9uUyquRjspxMsVZReecazLPAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bd23ba26455cd111922f8dae6c66940837bd1bcf3fb98fe0bc53129c1dd5d1a","last_reissued_at":"2026-05-17T23:46:45.805833Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:45.805833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A robust functional time series forecasting method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Han Lin Shang","submitted_at":"2019-01-17T23:16:34Z","abstract_excerpt":"Univariate time series often take the form of a collection of curves observed sequentially over time. Examples of these include hourly ground-level ozone concentration curves. These curves can be viewed as a time series of functions observed at equally spaced intervals over a dense grid. Since functional time series may contain various types of outliers, we introduce a robust functional time series forecasting method to down-weigh the influence of outliers in forecasting. Through a robust principal component analysis based on projection pursuit, a time series of functions can be decomposed int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.06030","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":""},"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":"1901.06030","created_at":"2026-05-17T23:46:45.805916+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.06030v1","created_at":"2026-05-17T23:46:45.805916+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.06030","created_at":"2026-05-17T23:46:45.805916+00:00"},{"alias_kind":"pith_short_12","alias_value":"TPJDXITEKXGR","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"TPJDXITEKXGRCGJC","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"TPJDXITE","created_at":"2026-05-18T12:33:30.264802+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/TPJDXITEKXGRCGJC7DNONRTJIC","json":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC.json","graph_json":"https://pith.science/api/pith-number/TPJDXITEKXGRCGJC7DNONRTJIC/graph.json","events_json":"https://pith.science/api/pith-number/TPJDXITEKXGRCGJC7DNONRTJIC/events.json","paper":"https://pith.science/paper/TPJDXITE"},"agent_actions":{"view_html":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC","download_json":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC.json","view_paper":"https://pith.science/paper/TPJDXITE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.06030&json=true","fetch_graph":"https://pith.science/api/pith-number/TPJDXITEKXGRCGJC7DNONRTJIC/graph.json","fetch_events":"https://pith.science/api/pith-number/TPJDXITEKXGRCGJC7DNONRTJIC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC/action/storage_attestation","attest_author":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC/action/author_attestation","sign_citation":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC/action/citation_signature","submit_replication":"https://pith.science/pith/TPJDXITEKXGRCGJC7DNONRTJIC/action/replication_record"}},"created_at":"2026-05-17T23:46:45.805916+00:00","updated_at":"2026-05-17T23:46:45.805916+00:00"}