{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:3NG4KWKVHOVLVFWMMBXB3WPP7U","short_pith_number":"pith:3NG4KWKV","canonical_record":{"source":{"id":"2606.27282","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"536a12700d1f65244f2a7c8fe21cc9051ad00911cd3624a9bd8867aee8f5b7a4","abstract_canon_sha256":"9b30b6b9da4a3faefcb48fb2f95aad642930972517fadc2388f991dfe68e461b"},"schema_version":"1.0"},"canonical_sha256":"db4dc559553baaba96cc606e1dd9effd255301cf55d24e0e68b96eb38c202e2c","source":{"kind":"arxiv","id":"2606.27282","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27282","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27282v1","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27282","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_12","alias_value":"3NG4KWKVHOVL","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_16","alias_value":"3NG4KWKVHOVLVFWM","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_8","alias_value":"3NG4KWKV","created_at":"2026-06-26T01:16:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:3NG4KWKVHOVLVFWMMBXB3WPP7U","target":"record","payload":{"canonical_record":{"source":{"id":"2606.27282","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"536a12700d1f65244f2a7c8fe21cc9051ad00911cd3624a9bd8867aee8f5b7a4","abstract_canon_sha256":"9b30b6b9da4a3faefcb48fb2f95aad642930972517fadc2388f991dfe68e461b"},"schema_version":"1.0"},"canonical_sha256":"db4dc559553baaba96cc606e1dd9effd255301cf55d24e0e68b96eb38c202e2c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:17.513012Z","signature_b64":"b0HEkMF7bRyAGWJ//1IVS96kF19SL1w6TfxilR7so7PgLYcQqkMDJlTMj9hKuuXGPW66cyRbKJmg3+Lart9gCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db4dc559553baaba96cc606e1dd9effd255301cf55d24e0e68b96eb38c202e2c","last_reissued_at":"2026-06-26T01:16:17.512591Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:17.512591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.27282","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":"2026-06-26T01:16:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IfAsyfdRqA67VJuH2SkGr81zyem8+xRttfURov1WwrDNPq6CSmJU+03nxNYUM42Px8F2oL2BVF3spA0U/L/fBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T13:49:43.857211Z"},"content_sha256":"45ee16e60b015b15a3e703284f1276d99d5d36e28f9224773a1ad50ef363053d","schema_version":"1.0","event_id":"sha256:45ee16e60b015b15a3e703284f1276d99d5d36e28f9224773a1ad50ef363053d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:3NG4KWKVHOVLVFWMMBXB3WPP7U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"How Good Can Linear Models Be for Time-Series Forecasting?","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jinglue Xu, Lang Huang, Luke Darlow","submitted_at":"2026-06-25T16:57:50Z","abstract_excerpt":"Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far lower cost by tuning preprocessing rather than scaling models. We use Ridge regression as the testbed, since it has a closed-form solution and interpretable weights, which let the optimal hyperparameters be read off the search directly. We search over context length, local normalization, regularization, and augmentati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27282","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/2606.27282/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":"2026-06-26T01:16:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I/wXD7kFNXJf+gABjHblUo+gD63noUdtBvPBfH804XNsUffQB0tNIHRvaN8Szyrs95BpjiWzMohAcSKOtJsbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T13:49:43.857600Z"},"content_sha256":"23cfa64359066eb06774d4d7ca0d7d5bbbaa1421e873809ba7c226a47c0007c8","schema_version":"1.0","event_id":"sha256:23cfa64359066eb06774d4d7ca0d7d5bbbaa1421e873809ba7c226a47c0007c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/bundle.json","state_url":"https://pith.science/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/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":"2026-07-01T13:49:43Z","links":{"resolver":"https://pith.science/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U","bundle":"https://pith.science/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/bundle.json","state":"https://pith.science/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3NG4KWKVHOVLVFWMMBXB3WPP7U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:3NG4KWKVHOVLVFWMMBXB3WPP7U","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":"9b30b6b9da4a3faefcb48fb2f95aad642930972517fadc2388f991dfe68e461b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:57:50Z","title_canon_sha256":"536a12700d1f65244f2a7c8fe21cc9051ad00911cd3624a9bd8867aee8f5b7a4"},"schema_version":"1.0","source":{"id":"2606.27282","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27282","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27282v1","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27282","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_12","alias_value":"3NG4KWKVHOVL","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_16","alias_value":"3NG4KWKVHOVLVFWM","created_at":"2026-06-26T01:16:17Z"},{"alias_kind":"pith_short_8","alias_value":"3NG4KWKV","created_at":"2026-06-26T01:16:17Z"}],"graph_snapshots":[{"event_id":"sha256:23cfa64359066eb06774d4d7ca0d7d5bbbaa1421e873809ba7c226a47c0007c8","target":"graph","created_at":"2026-06-26T01:16:17Z","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/2606.27282/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far lower cost by tuning preprocessing rather than scaling models. We use Ridge regression as the testbed, since it has a closed-form solution and interpretable weights, which let the optimal hyperparameters be read off the search directly. We search over context length, local normalization, regularization, and augmentati","authors_text":"Jinglue Xu, Lang Huang, Luke Darlow","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:57:50Z","title":"How Good Can Linear Models Be for Time-Series Forecasting?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27282","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":"sha256:45ee16e60b015b15a3e703284f1276d99d5d36e28f9224773a1ad50ef363053d","target":"record","created_at":"2026-06-26T01:16:17Z","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":"9b30b6b9da4a3faefcb48fb2f95aad642930972517fadc2388f991dfe68e461b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:57:50Z","title_canon_sha256":"536a12700d1f65244f2a7c8fe21cc9051ad00911cd3624a9bd8867aee8f5b7a4"},"schema_version":"1.0","source":{"id":"2606.27282","kind":"arxiv","version":1}},"canonical_sha256":"db4dc559553baaba96cc606e1dd9effd255301cf55d24e0e68b96eb38c202e2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db4dc559553baaba96cc606e1dd9effd255301cf55d24e0e68b96eb38c202e2c","first_computed_at":"2026-06-26T01:16:17.512591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-26T01:16:17.512591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b0HEkMF7bRyAGWJ//1IVS96kF19SL1w6TfxilR7so7PgLYcQqkMDJlTMj9hKuuXGPW66cyRbKJmg3+Lart9gCA==","signature_status":"signed_v1","signed_at":"2026-06-26T01:16:17.513012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.27282","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45ee16e60b015b15a3e703284f1276d99d5d36e28f9224773a1ad50ef363053d","sha256:23cfa64359066eb06774d4d7ca0d7d5bbbaa1421e873809ba7c226a47c0007c8"],"state_sha256":"fbb5f05951cea344ea0e4997adad5ee871bfe528ad01c53d0b766ff7e4e650db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cXnRfM/fJWMXk4gOvSPMQLv4Go76VEUIaWGagW6/AZp0vPXI2EaIP2+T1RZm8F5+/lM8WiF8IqWUv6zYE4gTDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T13:49:43.859607Z","bundle_sha256":"8d80194f18adfe5de6d8e2de8325b32e7f63c3e7f55989c68cd871120d275381"}}