{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AOED2L4ZBB5WRXJDGKLL2UTRFA","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":"66f6632577c56b49e0440908cb06debbc0d201a293f14d703b9691988fff8f53","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T03:22:16Z","title_canon_sha256":"d05f4f9f35b74cf418cda16dbb38c6d3a237c23fe4660a46343411477da5ef51"},"schema_version":"1.0","source":{"id":"2302.02077","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.02077","created_at":"2026-07-05T05:38:48Z"},{"alias_kind":"arxiv_version","alias_value":"2302.02077v1","created_at":"2026-07-05T05:38:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02077","created_at":"2026-07-05T05:38:48Z"},{"alias_kind":"pith_short_12","alias_value":"AOED2L4ZBB5W","created_at":"2026-07-05T05:38:48Z"},{"alias_kind":"pith_short_16","alias_value":"AOED2L4ZBB5WRXJD","created_at":"2026-07-05T05:38:48Z"},{"alias_kind":"pith_short_8","alias_value":"AOED2L4Z","created_at":"2026-07-05T05:38:48Z"}],"graph_snapshots":[{"event_id":"sha256:b8f08552bedc45ba4390636dd6017dae0c137ea3e55d6ab747a8a2aed338973b","target":"graph","created_at":"2026-07-05T05:38:48Z","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/2302.02077/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time series and generalize to new time series one-at-a-time. Previous approaches in meta-forecasting achieve competitive performance, but with the restriction of training a separate model for each sampling frequency. In this work, we investigate meta-forecasting over different sampling frequencies, and introduce a new model, the Continuous Frequency Adapter (CFA), specifically designed to learn frequency-invariant representati","authors_text":"Danielle C. Maddix, Hao Wang, Huibin Shen, Karthick Gopalswamy, Mike Van Ness, Xiaoyong Jin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T03:22:16Z","title":"Cross-Frequency Time Series Meta-Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02077","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:ba6102ec4f949982468685d2f81b76aa8ee9def5c8c58e6c4143c7b929930112","target":"record","created_at":"2026-07-05T05:38:48Z","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":"66f6632577c56b49e0440908cb06debbc0d201a293f14d703b9691988fff8f53","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-04T03:22:16Z","title_canon_sha256":"d05f4f9f35b74cf418cda16dbb38c6d3a237c23fe4660a46343411477da5ef51"},"schema_version":"1.0","source":{"id":"2302.02077","kind":"arxiv","version":1}},"canonical_sha256":"03883d2f99087b68dd233296bd527128209943b4b0a2aa1ea7c1acf51eef4217","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"03883d2f99087b68dd233296bd527128209943b4b0a2aa1ea7c1acf51eef4217","first_computed_at":"2026-07-05T05:38:48.603984Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:38:48.603984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3IflZPFTkWpeKZgxW03Caot7JvIahDpdv+K5wp6dTWTysIJuExSPj6JUL/bVz8RTOIsiBstqhE0VWkMYxybpBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:38:48.604498Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.02077","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba6102ec4f949982468685d2f81b76aa8ee9def5c8c58e6c4143c7b929930112","sha256:b8f08552bedc45ba4390636dd6017dae0c137ea3e55d6ab747a8a2aed338973b"],"state_sha256":"6eb4e42ba2dcef25d597018a8ca586a6ad442c4736b6a146356aece468f559f6"}