{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DZINU4Z63MOWNZAFW5XBE23KTA","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":"b844b2750a1d3dd5905a6d12a69364b5a500994b748b82532f0b30186020d697","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-26T09:53:20Z","title_canon_sha256":"5de54e04f0b2127104bca79b20404eb172e0570eaf392b1ba9292502a4a84d7e"},"schema_version":"1.0","source":{"id":"2504.18878","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.18878","created_at":"2026-07-05T10:54:23Z"},{"alias_kind":"arxiv_version","alias_value":"2504.18878v1","created_at":"2026-07-05T10:54:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18878","created_at":"2026-07-05T10:54:23Z"},{"alias_kind":"pith_short_12","alias_value":"DZINU4Z63MOW","created_at":"2026-07-05T10:54:23Z"},{"alias_kind":"pith_short_16","alias_value":"DZINU4Z63MOWNZAF","created_at":"2026-07-05T10:54:23Z"},{"alias_kind":"pith_short_8","alias_value":"DZINU4Z6","created_at":"2026-07-05T10:54:23Z"}],"graph_snapshots":[{"event_id":"sha256:2b34583fd106815495cdf3c303179c42ceb9a83b9111894ea5175afcaef585ab","target":"graph","created_at":"2026-07-05T10:54:23Z","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/2504.18878/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a temporal feature encoding architecture called Time Series Representation Model (TSRM) for multivariate time series forecasting and imputation. The architecture is structured around CNN-based representation layers, each dedicated to an independent representation learning task and designed to capture diverse temporal patterns, followed by an attention-based feature extraction layer and a merge layer, designed to aggregate extracted features. The architecture is fundamentally based on a configuration that is inspired by a Transformer encoder, with self-attention mechanisms at its c","authors_text":"Daniel Grillmeyer, Michael Stenger, Robert Leppich, Samuel Kounev, Vanessa Borst","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-26T09:53:20Z","title":"TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18878","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:fce24238ba6acb082e9b3082c8ab801c335a4e07b5a2d330baf15832678715f2","target":"record","created_at":"2026-07-05T10:54:23Z","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":"b844b2750a1d3dd5905a6d12a69364b5a500994b748b82532f0b30186020d697","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-26T09:53:20Z","title_canon_sha256":"5de54e04f0b2127104bca79b20404eb172e0570eaf392b1ba9292502a4a84d7e"},"schema_version":"1.0","source":{"id":"2504.18878","kind":"arxiv","version":1}},"canonical_sha256":"1e50da733edb1d66e405b76e126b6a981ca3bb2d5ccea8e01b2d9a34f40e4712","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e50da733edb1d66e405b76e126b6a981ca3bb2d5ccea8e01b2d9a34f40e4712","first_computed_at":"2026-07-05T10:54:23.035537Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:23.035537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NCI8fxtKK3ZIepojz6lDfgQdpn4Od/WbJQtOuMn5c3JOz+l+hdq1WXYqnMoQiaaL02WYpwEUhT9KsYF5j5lPCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:23.035954Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.18878","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fce24238ba6acb082e9b3082c8ab801c335a4e07b5a2d330baf15832678715f2","sha256:2b34583fd106815495cdf3c303179c42ceb9a83b9111894ea5175afcaef585ab"],"state_sha256":"43026b41a743bb9fce222033f8f1c7aeede9caec515c971619994049c6c5f2ce"}