{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GAUMWKOKOVNKKJH2MCERT4SOA5","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":"6d576f32316deaaa9fcba18a4f0755206f4ba467197fa3db585a76727d4d1476","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-09T05:20:48Z","title_canon_sha256":"f113dba7d2e0f5d51f29edc1aa53420f70563826024afaaef022bc95fdb180bc"},"schema_version":"1.0","source":{"id":"2403.05798","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.05798","created_at":"2026-07-05T08:41:00Z"},{"alias_kind":"arxiv_version","alias_value":"2403.05798v2","created_at":"2026-07-05T08:41:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.05798","created_at":"2026-07-05T08:41:00Z"},{"alias_kind":"pith_short_12","alias_value":"GAUMWKOKOVNK","created_at":"2026-07-05T08:41:00Z"},{"alias_kind":"pith_short_16","alias_value":"GAUMWKOKOVNKKJH2","created_at":"2026-07-05T08:41:00Z"},{"alias_kind":"pith_short_8","alias_value":"GAUMWKOK","created_at":"2026-07-05T08:41:00Z"}],"graph_snapshots":[{"event_id":"sha256:b62e250d1442206e2964391b27446d5c603f6ad1fa191dc06120c757680bbb0e","target":"graph","created_at":"2026-07-05T08:41:00Z","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/2403.05798/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, established through the pre-training, is still underexplored and may help yield more distinctive and informative representations to facilitate time series forecasting. To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space. We first desig","authors_text":"Anderson Schneider, Dongjin Song, Sahil Garg, Yuriy Nevmyvaka, Yushan Jiang, Zijie Pan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-09T05:20:48Z","title":"$\\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.05798","kind":"arxiv","version":2},"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:ac0974bdb97bceed3cfef2f0a5e67c192e08463f5744ee3a027476ec83898e66","target":"record","created_at":"2026-07-05T08:41:00Z","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":"6d576f32316deaaa9fcba18a4f0755206f4ba467197fa3db585a76727d4d1476","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-09T05:20:48Z","title_canon_sha256":"f113dba7d2e0f5d51f29edc1aa53420f70563826024afaaef022bc95fdb180bc"},"schema_version":"1.0","source":{"id":"2403.05798","kind":"arxiv","version":2}},"canonical_sha256":"3028cb29ca755aa524fa608919f24e07603357780149e058791fa2052c0651bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3028cb29ca755aa524fa608919f24e07603357780149e058791fa2052c0651bd","first_computed_at":"2026-07-05T08:41:00.497623Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:00.497623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/fAfLDjQFJrIKJnd3OOtBFMGC9q66gVr3TdLr8PJfhESOCl5V089uuwWEWGyGr50w1yQFmZuHudU2uiAGRfmDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:00.498098Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.05798","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac0974bdb97bceed3cfef2f0a5e67c192e08463f5744ee3a027476ec83898e66","sha256:b62e250d1442206e2964391b27446d5c603f6ad1fa191dc06120c757680bbb0e"],"state_sha256":"876c5c362f4d97f7f3f02621d29969782805cc39fbb91419f7311599096df5a0"}