{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MGQ6FBTRB4XMMXSZYIQWF3TMWC","short_pith_number":"pith:MGQ6FBTR","canonical_record":{"source":{"id":"2507.21155","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T19:30:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1f2c4083fad5437297da37962fc4109e8d0023ec3903c0883d2f5cbc3bd349c3","abstract_canon_sha256":"4b0e3d4269074e34ee4112ff8e60caf923712606110cd4938e0d1811de734de1"},"schema_version":"1.0"},"canonical_sha256":"61a1e286710f2ec65e59c22162ee6cb08035df187615825904af438d22a188f1","source":{"kind":"arxiv","id":"2507.21155","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.21155","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"arxiv_version","alias_value":"2507.21155v2","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.21155","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_12","alias_value":"MGQ6FBTRB4XM","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_16","alias_value":"MGQ6FBTRB4XMMXSZ","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_8","alias_value":"MGQ6FBTR","created_at":"2026-07-05T11:49:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MGQ6FBTRB4XMMXSZYIQWF3TMWC","target":"record","payload":{"canonical_record":{"source":{"id":"2507.21155","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T19:30:24Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1f2c4083fad5437297da37962fc4109e8d0023ec3903c0883d2f5cbc3bd349c3","abstract_canon_sha256":"4b0e3d4269074e34ee4112ff8e60caf923712606110cd4938e0d1811de734de1"},"schema_version":"1.0"},"canonical_sha256":"61a1e286710f2ec65e59c22162ee6cb08035df187615825904af438d22a188f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:11.977354Z","signature_b64":"0uvsbwqA2BbajlWpXaMuAnUnk/HCVyrFbRrtpH5ndjf7AvMw5NSyuJmfYKdrcG+1U1lSFLCRGk4QjovkOIbSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61a1e286710f2ec65e59c22162ee6cb08035df187615825904af438d22a188f1","last_reissued_at":"2026-07-05T11:49:11.976848Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:11.976848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.21155","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O5gpRcf5IJJIv7DnpfhnrD7OF31NeoY4BZX7bmcS/qTYED3XJRjm7Q5Kp68X8NpZv9Q4EQSFfLeqxmioI0FbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:09:04.914381Z"},"content_sha256":"f3e43ce09109234b35ebb91dc7d6042f06de5e39a0f99c4e78958aad3e3d7a4b","schema_version":"1.0","event_id":"sha256:f3e43ce09109234b35ebb91dc7d6042f06de5e39a0f99c4e78958aad3e3d7a4b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MGQ6FBTRB4XMMXSZYIQWF3TMWC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SPADE-S: A Sparsity-Robust Foundational Forecaster","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhinav Katoch, Hanjing Zhu, Kin G. Olivares, Malcolm Wolff, Matthew Li, Mengfei Cao, Michael W. Mahoney, Rahul Gopalsamy, Ravi Kiran Selvam, Roberto Bandarra, Ruijun Ma, Shankar Ramasubramanian, Sitan Yang, Stefania La Vattiata","submitted_at":"2025-07-24T19:30:24Z","abstract_excerpt":"Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-magnitude and sparse time series, including loss functions with implicit biases toward high-magnitude series, training-time sampling methods, and limitations of time series encoding methods.\n  SPADE-S is a robust forecasting architecture that significantly reduces magnitude- and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.21155","kind":"arxiv","version":2},"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/2507.21155/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":"2026-07-05T11:49:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8i9EuAhq8SWhF8CUvJdw/8zFx/QsNjWcDKgB4Wos/iDZmza/8BZ4/QHN2JJkswTlNdFUMw8zAvlD1X3IaCLhBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:09:04.914929Z"},"content_sha256":"cc16bcdb8a32ffad99ef5a5359928c0a615dbfebc13eb7230a6f07bda226c590","schema_version":"1.0","event_id":"sha256:cc16bcdb8a32ffad99ef5a5359928c0a615dbfebc13eb7230a6f07bda226c590"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/bundle.json","state_url":"https://pith.science/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/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":"2026-08-09T20:09:04Z","links":{"resolver":"https://pith.science/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC","bundle":"https://pith.science/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/bundle.json","state":"https://pith.science/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MGQ6FBTRB4XMMXSZYIQWF3TMWC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MGQ6FBTRB4XMMXSZYIQWF3TMWC","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":"4b0e3d4269074e34ee4112ff8e60caf923712606110cd4938e0d1811de734de1","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T19:30:24Z","title_canon_sha256":"1f2c4083fad5437297da37962fc4109e8d0023ec3903c0883d2f5cbc3bd349c3"},"schema_version":"1.0","source":{"id":"2507.21155","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.21155","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"arxiv_version","alias_value":"2507.21155v2","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.21155","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_12","alias_value":"MGQ6FBTRB4XM","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_16","alias_value":"MGQ6FBTRB4XMMXSZ","created_at":"2026-07-05T11:49:11Z"},{"alias_kind":"pith_short_8","alias_value":"MGQ6FBTR","created_at":"2026-07-05T11:49:11Z"}],"graph_snapshots":[{"event_id":"sha256:cc16bcdb8a32ffad99ef5a5359928c0a615dbfebc13eb7230a6f07bda226c590","target":"graph","created_at":"2026-07-05T11:49:11Z","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/2507.21155/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-magnitude and sparse time series, including loss functions with implicit biases toward high-magnitude series, training-time sampling methods, and limitations of time series encoding methods.\n  SPADE-S is a robust forecasting architecture that significantly reduces magnitude- and ","authors_text":"Abhinav Katoch, Hanjing Zhu, Kin G. Olivares, Malcolm Wolff, Matthew Li, Mengfei Cao, Michael W. Mahoney, Rahul Gopalsamy, Ravi Kiran Selvam, Roberto Bandarra, Ruijun Ma, Shankar Ramasubramanian, Sitan Yang, Stefania La Vattiata","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T19:30:24Z","title":"SPADE-S: A Sparsity-Robust Foundational Forecaster"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.21155","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:f3e43ce09109234b35ebb91dc7d6042f06de5e39a0f99c4e78958aad3e3d7a4b","target":"record","created_at":"2026-07-05T11:49:11Z","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":"4b0e3d4269074e34ee4112ff8e60caf923712606110cd4938e0d1811de734de1","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T19:30:24Z","title_canon_sha256":"1f2c4083fad5437297da37962fc4109e8d0023ec3903c0883d2f5cbc3bd349c3"},"schema_version":"1.0","source":{"id":"2507.21155","kind":"arxiv","version":2}},"canonical_sha256":"61a1e286710f2ec65e59c22162ee6cb08035df187615825904af438d22a188f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61a1e286710f2ec65e59c22162ee6cb08035df187615825904af438d22a188f1","first_computed_at":"2026-07-05T11:49:11.976848Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:11.976848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0uvsbwqA2BbajlWpXaMuAnUnk/HCVyrFbRrtpH5ndjf7AvMw5NSyuJmfYKdrcG+1U1lSFLCRGk4QjovkOIbSCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:11.977354Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.21155","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3e43ce09109234b35ebb91dc7d6042f06de5e39a0f99c4e78958aad3e3d7a4b","sha256:cc16bcdb8a32ffad99ef5a5359928c0a615dbfebc13eb7230a6f07bda226c590"],"state_sha256":"919bb9eee23d0a604783889e8a45271150219df9d5e402c441407a6a0353885b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"owoA1Gq+zh0HWtoRXV2xHVF7cf2S6DvXUr9pPRMmzJDZOGGHft8wUhE+Qn5It57NTTDqokNV8rs8819N4N8aAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T20:09:04.920535Z","bundle_sha256":"3f4c2797869fb10032b722797f7dfd996afa9c202a0d9cfa5978ebdb66ce6e22"}}