{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:PLKMRSBGIGY3JWTUWJTJ77NP6Y","short_pith_number":"pith:PLKMRSBG","canonical_record":{"source":{"id":"2412.11171","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T12:41:53Z","cross_cats_sorted":[],"title_canon_sha256":"75dfbb9e2a2a40b0d3ecbf2028dc0281f6fe28ab0a437fe1ef93bb3e7fc2a366","abstract_canon_sha256":"6d82da123905d4a9a1e98f1082504c326b5674124bbceef93f2267d52aa73c49"},"schema_version":"1.0"},"canonical_sha256":"7ad4c8c82641b1b4da74b2669ffdaff63c046d02b2fe3d5f2f41c74f8cc88882","source":{"kind":"arxiv","id":"2412.11171","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.11171","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"arxiv_version","alias_value":"2412.11171v1","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11171","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_12","alias_value":"PLKMRSBGIGY3","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_16","alias_value":"PLKMRSBGIGY3JWTU","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_8","alias_value":"PLKMRSBG","created_at":"2026-07-05T09:49:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:PLKMRSBGIGY3JWTUWJTJ77NP6Y","target":"record","payload":{"canonical_record":{"source":{"id":"2412.11171","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T12:41:53Z","cross_cats_sorted":[],"title_canon_sha256":"75dfbb9e2a2a40b0d3ecbf2028dc0281f6fe28ab0a437fe1ef93bb3e7fc2a366","abstract_canon_sha256":"6d82da123905d4a9a1e98f1082504c326b5674124bbceef93f2267d52aa73c49"},"schema_version":"1.0"},"canonical_sha256":"7ad4c8c82641b1b4da74b2669ffdaff63c046d02b2fe3d5f2f41c74f8cc88882","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:28.478805Z","signature_b64":"TEwxEvZV6yE56aXX0a4EUQio1zQ+o2Z4zCeigl3mZ+UIul+SyRzW9lAhKaR99Ru23lyf+Rxyb7BL94nl6cePBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ad4c8c82641b1b4da74b2669ffdaff63c046d02b2fe3d5f2f41c74f8cc88882","last_reissued_at":"2026-07-05T09:49:28.478431Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:28.478431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.11171","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":"2026-07-05T09:49:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fiVwED51zepTC4H+fgme0gNqeqsfEjrcv8UYXtD0gpvTes5vYVIgPWVSSk22CcVZBoc7Z8V+9sIn0QCXFZnWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T01:01:37.630734Z"},"content_sha256":"c5f619409fe625b1782ef4c8e0d52265851da70894f563ee4a429782c7ae23f8","schema_version":"1.0","event_id":"sha256:c5f619409fe625b1782ef4c8e0d52265851da70894f563ee4a429782c7ae23f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:PLKMRSBGIGY3JWTUWJTJ77NP6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Latent Spaces for Domain Generalization in Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maarten de Rijke, Songgaojun Deng","submitted_at":"2024-12-15T12:41:53Z","abstract_excerpt":"Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains -- such as forecasting web traffic data on new platforms/websites or estimating e-commerce demand in new regions -- remains underexplored. Existing forecasting models often struggle with domain shifts in time series data, as the temporal patterns involve complex components like trends, seasonality, etc. While some prior work addresses this by matching feature distributions across domains or disentangling domain-shared features using label information, they fai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11171","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/2412.11171/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":"2026-07-05T09:49:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oyB/dZhW02gZ662dA6TzmVICSMlaFH9yGyLmqhpNisPBBCpfm520R3lNRQIN68zHq3CDzsI3pywq8+a2/eHCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T01:01:37.631242Z"},"content_sha256":"ba1032a2ddbaebd53d1c293f9bc4adef0c47e252b9d3e96f52147728422f2d30","schema_version":"1.0","event_id":"sha256:ba1032a2ddbaebd53d1c293f9bc4adef0c47e252b9d3e96f52147728422f2d30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/bundle.json","state_url":"https://pith.science/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/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":"2026-08-13T01:01:37Z","links":{"resolver":"https://pith.science/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y","bundle":"https://pith.science/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/bundle.json","state":"https://pith.science/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PLKMRSBGIGY3JWTUWJTJ77NP6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PLKMRSBGIGY3JWTUWJTJ77NP6Y","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":"6d82da123905d4a9a1e98f1082504c326b5674124bbceef93f2267d52aa73c49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T12:41:53Z","title_canon_sha256":"75dfbb9e2a2a40b0d3ecbf2028dc0281f6fe28ab0a437fe1ef93bb3e7fc2a366"},"schema_version":"1.0","source":{"id":"2412.11171","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.11171","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"arxiv_version","alias_value":"2412.11171v1","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11171","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_12","alias_value":"PLKMRSBGIGY3","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_16","alias_value":"PLKMRSBGIGY3JWTU","created_at":"2026-07-05T09:49:28Z"},{"alias_kind":"pith_short_8","alias_value":"PLKMRSBG","created_at":"2026-07-05T09:49:28Z"}],"graph_snapshots":[{"event_id":"sha256:ba1032a2ddbaebd53d1c293f9bc4adef0c47e252b9d3e96f52147728422f2d30","target":"graph","created_at":"2026-07-05T09:49:28Z","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/2412.11171/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains -- such as forecasting web traffic data on new platforms/websites or estimating e-commerce demand in new regions -- remains underexplored. Existing forecasting models often struggle with domain shifts in time series data, as the temporal patterns involve complex components like trends, seasonality, etc. While some prior work addresses this by matching feature distributions across domains or disentangling domain-shared features using label information, they fai","authors_text":"Maarten de Rijke, Songgaojun Deng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T12:41:53Z","title":"Learning Latent Spaces for Domain Generalization in Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11171","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:c5f619409fe625b1782ef4c8e0d52265851da70894f563ee4a429782c7ae23f8","target":"record","created_at":"2026-07-05T09:49:28Z","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":"6d82da123905d4a9a1e98f1082504c326b5674124bbceef93f2267d52aa73c49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T12:41:53Z","title_canon_sha256":"75dfbb9e2a2a40b0d3ecbf2028dc0281f6fe28ab0a437fe1ef93bb3e7fc2a366"},"schema_version":"1.0","source":{"id":"2412.11171","kind":"arxiv","version":1}},"canonical_sha256":"7ad4c8c82641b1b4da74b2669ffdaff63c046d02b2fe3d5f2f41c74f8cc88882","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ad4c8c82641b1b4da74b2669ffdaff63c046d02b2fe3d5f2f41c74f8cc88882","first_computed_at":"2026-07-05T09:49:28.478431Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:49:28.478431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TEwxEvZV6yE56aXX0a4EUQio1zQ+o2Z4zCeigl3mZ+UIul+SyRzW9lAhKaR99Ru23lyf+Rxyb7BL94nl6cePBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:49:28.478805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.11171","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c5f619409fe625b1782ef4c8e0d52265851da70894f563ee4a429782c7ae23f8","sha256:ba1032a2ddbaebd53d1c293f9bc4adef0c47e252b9d3e96f52147728422f2d30"],"state_sha256":"2e65656438142f950e163726bce2c5d97399597b0aad8fb79ac156120156812d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"77qk1ogSx8tvTewkxBeK4iIlPIPlH2XOOhvzt9Iex/QxVWnOG+De497Tv1M1VsbWN2dXnzu9loZZhFhUO/TFCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T01:01:37.636823Z","bundle_sha256":"0a5e6445526a8ad5051c7360a3fbb2d005905596fafe9ce9bfea0754591811cc"}}