{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RGEAKUOKEK45OPSHAOPBODZZJ6","short_pith_number":"pith:RGEAKUOK","canonical_record":{"source":{"id":"2507.05891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T11:26:42Z","cross_cats_sorted":[],"title_canon_sha256":"4badf669a0650bea6da1d7d03c36c3320cae2a0f94173acbc3455bd09a548088","abstract_canon_sha256":"e0146496a6a1c2082165af2f0f748c307376354ea3cf333e4e3028cc7ec99660"},"schema_version":"1.0"},"canonical_sha256":"89880551ca22b9d73e47039e170f394f954060241b35539c342b5ade67df893e","source":{"kind":"arxiv","id":"2507.05891","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05891","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05891v1","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05891","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_12","alias_value":"RGEAKUOKEK45","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"RGEAKUOKEK45OPSH","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"RGEAKUOK","created_at":"2026-07-05T11:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RGEAKUOKEK45OPSHAOPBODZZJ6","target":"record","payload":{"canonical_record":{"source":{"id":"2507.05891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T11:26:42Z","cross_cats_sorted":[],"title_canon_sha256":"4badf669a0650bea6da1d7d03c36c3320cae2a0f94173acbc3455bd09a548088","abstract_canon_sha256":"e0146496a6a1c2082165af2f0f748c307376354ea3cf333e4e3028cc7ec99660"},"schema_version":"1.0"},"canonical_sha256":"89880551ca22b9d73e47039e170f394f954060241b35539c342b5ade67df893e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:37.850439Z","signature_b64":"mQWJJQGE29ICVx8eTaz7uXWuiMVCYEMJ+cLhl5Y6S28bcbMhpJQUvABwqyPJjtJunQYYreCw+pxhBQ+OjUIDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89880551ca22b9d73e47039e170f394f954060241b35539c342b5ade67df893e","last_reissued_at":"2026-07-05T11:33:37.849922Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:37.849922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.05891","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:33:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0DcsJAQGFP3pWMSddJRMZsEFkiJwf38F0bWIqpbTS/HHG2Px69cOgrySXSLBWZjzWQIM7wNtSVo9dR08YEYBBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:51:43.706440Z"},"content_sha256":"ee052a7ace019d178aa0aae1761c64352e50668e0abc158b9a0af5122c5a4c86","schema_version":"1.0","event_id":"sha256:ee052a7ace019d178aa0aae1761c64352e50668e0abc158b9a0af5122c5a4c86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RGEAKUOKEK45OPSHAOPBODZZJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andr\\'e Bauer, Michael Stenger, Robert Leppich, Samuel Kounev","submitted_at":"2025-07-08T11:26:42Z","abstract_excerpt":"With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projection. Time series forecasting remains a challenging task, demanding effective sequence representation, meaningful information extraction, and precise future projection. Each dataset and forecasting configuration constitutes a distinct task, each posing unique challenges the model must overcome to produce accurate predictions. To systematically address these task-specific difficult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05891","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.05891/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:33:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1krwi5chRWMryN2WiDtrkeu57Li8fcM+j5Dux6QbqM9V5Idya0Ho1w0aRvfmx3qOpH2IdqOo2Dr99luAnoBSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:51:43.707003Z"},"content_sha256":"51419ca8e53c48194e4794a84298797c13602e2429635d0694374a5d1821fb7d","schema_version":"1.0","event_id":"sha256:51419ca8e53c48194e4794a84298797c13602e2429635d0694374a5d1821fb7d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/bundle.json","state_url":"https://pith.science/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T03:51:43Z","links":{"resolver":"https://pith.science/pith/RGEAKUOKEK45OPSHAOPBODZZJ6","bundle":"https://pith.science/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/bundle.json","state":"https://pith.science/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RGEAKUOKEK45OPSHAOPBODZZJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RGEAKUOKEK45OPSHAOPBODZZJ6","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":"e0146496a6a1c2082165af2f0f748c307376354ea3cf333e4e3028cc7ec99660","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T11:26:42Z","title_canon_sha256":"4badf669a0650bea6da1d7d03c36c3320cae2a0f94173acbc3455bd09a548088"},"schema_version":"1.0","source":{"id":"2507.05891","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05891","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05891v1","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05891","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_12","alias_value":"RGEAKUOKEK45","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"RGEAKUOKEK45OPSH","created_at":"2026-07-05T11:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"RGEAKUOK","created_at":"2026-07-05T11:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:51419ca8e53c48194e4794a84298797c13602e2429635d0694374a5d1821fb7d","target":"graph","created_at":"2026-07-05T11:33:37Z","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/2507.05891/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projection. Time series forecasting remains a challenging task, demanding effective sequence representation, meaningful information extraction, and precise future projection. Each dataset and forecasting configuration constitutes a distinct task, each posing unique challenges the model must overcome to produce accurate predictions. To systematically address these task-specific difficult","authors_text":"Andr\\'e Bauer, Michael Stenger, Robert Leppich, Samuel Kounev","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T11:26:42Z","title":"Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05891","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:ee052a7ace019d178aa0aae1761c64352e50668e0abc158b9a0af5122c5a4c86","target":"record","created_at":"2026-07-05T11:33:37Z","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":"e0146496a6a1c2082165af2f0f748c307376354ea3cf333e4e3028cc7ec99660","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-08T11:26:42Z","title_canon_sha256":"4badf669a0650bea6da1d7d03c36c3320cae2a0f94173acbc3455bd09a548088"},"schema_version":"1.0","source":{"id":"2507.05891","kind":"arxiv","version":1}},"canonical_sha256":"89880551ca22b9d73e47039e170f394f954060241b35539c342b5ade67df893e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89880551ca22b9d73e47039e170f394f954060241b35539c342b5ade67df893e","first_computed_at":"2026-07-05T11:33:37.849922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:37.849922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mQWJJQGE29ICVx8eTaz7uXWuiMVCYEMJ+cLhl5Y6S28bcbMhpJQUvABwqyPJjtJunQYYreCw+pxhBQ+OjUIDCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:37.850439Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.05891","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee052a7ace019d178aa0aae1761c64352e50668e0abc158b9a0af5122c5a4c86","sha256:51419ca8e53c48194e4794a84298797c13602e2429635d0694374a5d1821fb7d"],"state_sha256":"b968bb11d514e5f712ac24634dd8b608d943f8135eb1ade05e5c68c1c4e17860"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"asf8NDYU5DdvIrUqVKC2ZwAyeh2shH5Zef/rCs5WYHpthfT2Zz7v1HkozXWNNNTHisVvie//IyJNx1AWBJ3xCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:51:43.711308Z","bundle_sha256":"0c8192e0b24bb4cd8290f10787c4ef8d9dfac601040e84b7a60fdb5244bb6b23"}}