{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BSMMKZZAIGYP4SX7ZTZQOBBW5D","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":"f7e5d67e7ebf26071391e3556cec99f1f6c20e3900cdd04aaeefcc582328dca7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-24T07:44:39Z","title_canon_sha256":"7205feea6b250d76a07d61bf9164d2c2c6ea80032e5cbf4a81aeba2e0a313171"},"schema_version":"1.0","source":{"id":"2411.15743","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.15743","created_at":"2026-07-05T09:39:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.15743v1","created_at":"2026-07-05T09:39:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15743","created_at":"2026-07-05T09:39:40Z"},{"alias_kind":"pith_short_12","alias_value":"BSMMKZZAIGYP","created_at":"2026-07-05T09:39:40Z"},{"alias_kind":"pith_short_16","alias_value":"BSMMKZZAIGYP4SX7","created_at":"2026-07-05T09:39:40Z"},{"alias_kind":"pith_short_8","alias_value":"BSMMKZZA","created_at":"2026-07-05T09:39:40Z"}],"graph_snapshots":[{"event_id":"sha256:16f013f5eac990aaecbe8771ba97f525c12169b29ff778706a96a53256db0bdf","target":"graph","created_at":"2026-07-05T09:39:40Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.15743/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they struggle when data is scarce or not available at all, motivating the emergence of zero-shot and few-shot learning settings. Recent advancements often leverage large-scale foundation models for such tasks, but these methods require extensive data and compute resources, and their performance may be hindered by ineffective learning from the available train","authors_text":"Dotan Di Castro, Liran Nochumsohn, Michal Moshkovitz, Omri Azencot, Orly Avner","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-24T07:44:39Z","title":"Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15743","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":"sha256:2dc8cd2ecef934eff9fe4171c7cc8493716d1477dfe89429e5780e6a68e54fca","target":"record","created_at":"2026-07-05T09:39:40Z","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":"f7e5d67e7ebf26071391e3556cec99f1f6c20e3900cdd04aaeefcc582328dca7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-24T07:44:39Z","title_canon_sha256":"7205feea6b250d76a07d61bf9164d2c2c6ea80032e5cbf4a81aeba2e0a313171"},"schema_version":"1.0","source":{"id":"2411.15743","kind":"arxiv","version":1}},"canonical_sha256":"0c98c5672041b0fe4affccf3070436e8c6aedf216e4675628d2db36862206390","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c98c5672041b0fe4affccf3070436e8c6aedf216e4675628d2db36862206390","first_computed_at":"2026-07-05T09:39:40.395367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:39:40.395367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pb89eYziEVuwB/Id20NdKiHojP8lptDJDc6YOro1RBgE6WUPsOh5EE8PIxxIFPumlWmiaGmGtXcuxCOqYJSVBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:39:40.396868Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.15743","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2dc8cd2ecef934eff9fe4171c7cc8493716d1477dfe89429e5780e6a68e54fca","sha256:16f013f5eac990aaecbe8771ba97f525c12169b29ff778706a96a53256db0bdf"],"state_sha256":"eae4e7854b84b6be391f918880370f888973b3728bbf27d46d7bd02dd0bcf19d"}